A distribution-micro-cooperative optimization scheduling method and device for integrated differentiated building complexes

Abstract

A distribution-micro-cooperative optimization scheduling method and device integrating differentiated building groups, the method establishes a distribution network-level flexibility market clearing model, including a flexibility aggregator service declaration optimization sub-model and a distribution network operator flexibility service procurement planning sub-model; through the flexibility aggregator service declaration optimization sub-model, the distribution network node load baseline and the user energy baseline obtained by the day-ahead energy management baseline scheduling optimization model are used as the benchmark to optimize the flexibility service transaction plan that takes into account the differentiated interests of both the flexibility aggregator and the upper-level distribution network operator; through the distribution network operator flexibility service procurement planning sub-model, the distribution network operator plans to purchase flexibility services from the flexibility aggregator; the ADMM algorithm is used to solve the distribution network-level flexibility market clearing model to obtain the microgrid optimization scheduling result. The present invention can realize the efficient and economic operation of the distribution network-level flexibility market; and optimize the scheduling of the congestion management of the distribution network.

Description

Technical Field

[0001] The invention relates to a distribution-micro-coordinated optimization dispatching method for an integrated differentiated building complex, and belongs to the technical field of micro-grids. Background Art

[0002] At present, when optimizing the operation of microgrids integrated with intelligent buildings, the energy consumption plan formulated at the microgrid system level often fails to meet the operation constraints of the distribution network, resulting in problems such as overload of distribution network lines and low node voltage. Due to the existence of indoor temperature comfort range and building thermal inertia, the power load of the building heating, ventilation and air conditioning (HVAC) system has the potential for flexible regulation, which allows building cluster users to have flexibility at certain times.

[0003] In the related technology, the two-layer optimization based on the master-slave game utilizes the differences in building insulation performance and the two-layer optimization framework of the integration of buildings and heating networks in the community integrated energy system. Due to the existence of the indoor temperature comfort range and the thermal inertia of the building, the power load of the building HVAC system has the potential for flexible regulation, which allows users of the building cluster to have flexibility at some times. However, for the distribution network, the flexibility provided by the building cluster as a unit is limited and too scattered. Therefore, the flexibility aggregator integrates the same type of building clusters built by the same developer into a response group that can be uniformly controlled, gathers flexibility resources and provides flexibility services to the distribution network, thereby performing congestion management. How to achieve congestion management of the distribution network for optimized scheduling so that both the aggregator and the user are profitable, and optimizes the power and price of providing flexibility services has practical application significance. Summary of the invention

[0004] To this end, the present invention provides a distribution-micro-coordinated optimization scheduling method and device integrating differentiated building clusters, which solves the problem that the flexibility provided by traditional technologies based on building clusters is limited and too scattered, and cannot take into account the interests of three different entities: distribution system operators (DSOs), flexibility aggregators and differentiated building cluster users.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a distribution-micro-coordinated optimization scheduling method integrating differentiated building groups, comprising:

[0006] Establishing a multi-agent hierarchical model of a distribution network, in which flexibility aggregators and microgrid system operators aggregate building flexibility resources according to user types and line locations; the constraints of the multi-agent hierarchical model of a distribution network include power flow constraints, distribution network line load constraints, and distribution network node voltage constraints;

[0007] Establish a building cluster user microgrid energy management model, the optimization goal of the building cluster user microgrid energy management model is to maximize the microgrid operation income, and the constraints of the building cluster user microgrid energy management model include single heating area heat balance constraints, indoor temperature comfort range constraints, HVAC system constraints, power purchase price constraints, microgrid power balance constraints, power sales price constraints and MSO network operation constraints; through the building cluster user microgrid energy management model, the microgrid system operator and the building cluster users in the distribution network multi-agent hierarchical model are collaboratively managed;

[0008] Establish a day-ahead energy management baseline scheduling optimization model, characterize the optimal interactive behavior between the microgrid system operator and the building cluster users in the building cluster user microgrid energy management model through the day-ahead energy management baseline scheduling optimization model, and obtain the distribution network node load baseline and user energy consumption baseline of the distribution network-level flexibility market clearing model in the distribution network multi-agent hierarchical model;

[0009] A distribution network-level flexibility market clearing model is established, wherein the distribution network-level flexibility market clearing model includes a flexibility aggregator service declaration optimization sub-model and a distribution network operator flexibility service procurement planning sub-model; through the flexibility aggregator service declaration optimization sub-model, based on the distribution network node load baseline and user energy consumption baseline obtained by the day-ahead energy management baseline scheduling optimization model, an optimization decision is made on the flexibility service transaction plan that takes into account the differentiated interests of both the flexibility aggregator and the upper-level distribution network operator; through the distribution network operator flexibility service procurement planning sub-model, the distribution network operator is planned to purchase flexibility services from the flexibility aggregator;

[0010] The ADMM algorithm is used to solve the distribution network-level flexibility market clearing model, and a distribution-micro-coordinated optimization scheduling result integrating differentiated building complexes is obtained.

[0011] As a preferred scheme for the distribution-micro-cooperative optimization dispatching method integrating differentiated building groups, the power flow constraint formula of the multi-agent hierarchical model of the distribution network is:

[0012]

[0013] 1-ε≤V m ≤1+ε

[0014] Where P m+1 is the active power injected into node m+1; P m is the active power injected into node m; Q m+1 is the reactive power injected into node m+1; Q m is the reactive power injected into node m; V m+1is the node voltage injected into node m+1; V m is the node voltage injected into node m; is the active power consumed by the load injected into node m+1; is the reactive power consumed by the load injected into node m+1; r f 、x f are the resistance and reactance between nodes m and m+1 respectively; ε is the offset margin of the node voltage.

[0015] As a preferred scheme for the distribution-micro-cooperative optimization dispatching method integrating differentiated building groups, the distribution network line load constraint formula of the distribution network multi-agent hierarchical model is:

[0016]

[0017] |F l,t |≤F l max

[0018] Where P n,t is the active load power at node n at time t; G n,l is the element in the nth row and lth column of the distribution network correlation matrix; F l max is the upper limit of the rated load of line l; F l,t is the load flowing through line l at time t;

[0019] The distribution network node voltage constraint formula of the distribution network multi-agent hierarchical model is:

[0020]

[0021]

[0022] In the formula, Q n,t is the reactive load power at node n at time t; is the power factor of node n; V n,t is the node voltage of node n at time t; V s is the bus voltage on the low voltage side of the substation; n,n* is the impedance of the line between nodes n and n*; V min is the lower limit of the node voltage amplitude; j is the imaginary unit is the active load power of nodes except node n; is the reactive load power except node n.

[0023] As a preferred scheme for the distribution-micro-cooperative optimization scheduling method integrating differentiated building clusters, the expression formula of the single heating area heat balance constraint of the building cluster user microgrid energy management model is:

[0024]

[0025] In the formula, the subscript ag is the building cluster number; are the heat capacity, midpoint temperature and thermal resistance of the wall between the heating area and air node j respectively; α 1,j , are the heat absorption rate, surface area and light intensity of the wall respectively; r 1,j It is a binary variable, which takes the value of 1 when the wall is exposed to light and 0 otherwise; τ win are the thermal resistance, area and transmittance of the window respectively; is the temperature of air node j; is the temperature of the indoor air in the heating area; c air 、m r , They are indoor air heat capacity, air specific heat capacity, HVAC supply air flow rate and supply air temperature in the heating area; They are random heat gain of indoor air and light radiation heat gain in the heating area respectively; j=1,2,3,4,5.

[0026] As a preferred scheme for the distribution-micro-coordinated optimization scheduling method integrating differentiated building clusters, the expression formula of the indoor temperature comfort range constraint of the building cluster user micro-grid energy management model is:

[0027]

[0028] In the formula, subscript s is the heating zone number, and t is the time number; is the temperature of the indoor air in the heating area; T r ,min , T r,max are the lower and upper limits of the indoor temperature comfort range respectively;

[0029] The expression formula of the HVAC system constraint of the building cluster user microgrid energy management model is:

[0030]

[0031] In the formula, is the HVAC supply air temperature; T t h,min , T t h,max are the lower limit and upper limit of the HVAC supply air outlet air temperature at time t, respectively; are the power purchased from the microgrid by the single heating area at time t, the power consumed by HVAC, and the power consumed by uncontrollable electrical equipment; mr is the HVAC supply air flow; c air is the specific heat capacity of air; C COP is the energy efficiency ratio of the HVAC equipment;

[0032] The expression formula of the power purchase price constraint of the building cluster user microgrid energy management model is:

[0033]

[0034] In the formula, is the electricity price sold by the microgrid to users at time t; are the lower and upper limits of the microgrid electricity price at time t; C sale,avg is the upper limit of the average electricity selling price of the microgrid; T is the number of control cycles in a day;

[0035] The MSO network operation constraints of the building cluster user microgrid energy management model are the same as the power flow constraints of the distribution network multi-agent hierarchical model;

[0036] The expression formula of the building cluster user optimization target of the building cluster user microgrid energy management model is:

[0037]

[0038] In the formula, OC ag is the energy cost of the users in the lower building cluster ag; Δt is the duration of a single control cycle; is the electricity purchase price of the user at time t; is the power purchased by the single heating area from the microgrid at time t

[0039] The microgrid power balance constraint of the building cluster user microgrid energy management model is:

[0040]

[0041] Where P t , P t pv and They are the power purchased by the microgrid from the upper distribution network at time t, the total output of the distributed PV system in the microgrid, and the power sold by the microgrid to the single heating area;

[0042] The power selling price constraint formula of the building cluster user microgrid energy management model is:

[0043]

[0044] In the formula, is the electricity price sold by the microgrid to users at time t; and are the lower and upper limits of the microgrid electricity price at time t; C sale,avg It is the upper limit of the average electricity selling price of the microgrid.

[0045] The MSO optimization objective formula of the building cluster user microgrid energy management model is:

[0046]

[0047] In the formula, OM is the income of the upper-level MSO, C t is the price of electricity purchased by the microgrid system from the upper distribution network at time t; is the electricity price sold by the microgrid to users at time t; P is the power sold by the microgrid to the single heating area at time t; t is the power purchased by the microgrid from the distribution network at time t.

[0048] As a preferred scheme for the distribution-micro-cooperative optimization scheduling method integrating differentiated building clusters, the optimal interactive behavior between the microgrid system operator and the building cluster users in the building cluster user microgrid energy management model is characterized by the day-ahead energy management baseline scheduling optimization model, and the distribution network node load baseline and user energy baseline of the distribution network-level flexibility market clearing model in the distribution network multi-agent hierarchical model are obtained as follows:

[0049]

[0050] In the formula, is the day-ahead load dispatch baseline power at node n at time t; is the HVAC equipment load power of heating area b at time t in the day-ahead dispatch baseline plan; is the price at which the building cluster users aggregated by aggregator a at node n purchase electricity from the microgrid system operator MSO at time t in the day-ahead dispatch baseline solution.

[0051] As a preferred solution for the distribution-micro-cooperative optimization scheduling method integrating differentiated building groups, the objective function formula of the flexibility aggregator service declaration optimization sub-model is:

[0052]

[0053] In the formula, OA a The revenue for flexibility aggregators to provide flexibility services to the distribution network; For the set of nodes that require flexibility; is the set of heating areas of the building cluster aggregated by aggregator a under the microgrid system connected to node n; is the price at which aggregator a provides flexibility at node n at time t; is the total amount of flexibility provided by aggregator a at node n at time t; is the flexibility cost coefficient of heating zone b at time t; The flexibility power provided to the HVAC equipment load in heating zone b at time t; The incremental energy cost of heating area b due to providing flexibility at time t;

[0054] The constraints of the flexibility aggregator service application optimization sub-model include flexibility aggregation constraints, compensation power constraints, HVAC equipment load constraints, flexibility selling price constraints, user energy cost incremental compensation constraints and user basic energy demand constraints.

[0055] As a preferred solution for the distribution-micro-cooperative optimization scheduling method integrating differentiated building groups, the objective function formula of the distribution network operator flexibility service procurement planning sub-model is:

[0056]

[0057] Where OD is the cost of purchasing flexibility services by the distribution system operator DSO; are the price and power of flexibility purchased by DSO from aggregator a at time t; N ag is the aggregation business set; is the blocking time set of node n;

[0058] The constraints of the distribution network operator flexibility service procurement planning sub-model include node load power constraints, flexibility procurement price constraints and distribution network operation constraints.

[0059] As a preferred solution of the distribution-micro-cooperative optimization dispatching method for integrated differentiated building complexes, the steps of using the ADMM algorithm to solve the distribution network-level flexibility market clearing model and obtaining a distribution-micro-cooperative optimization dispatching result for an integrated differentiated building complex include:

[0060] Set initial values ​​for global variables, dual variables, residuals, dual residuals, and penalty factors;

[0061] Solve the DSO optimization subproblem and the flexibility aggregator optimization subproblem;

[0062] Update global variables, dual variables, residuals, dual residuals, and penalty factors;

[0063] Check whether both the residual and the dual residual meet the convergence judgment conditions. If so, stop the iteration and output the results. If not, re-optimize the distribution system operator DSO subproblem and the flexibility aggregator subproblem.

[0064] The present invention also provides a distribution-micro-cooperative optimization scheduling device for an integrated differentiated building complex, which adopts the distribution-micro-cooperative optimization scheduling method for an integrated differentiated building complex, and comprises:

[0065] A distribution network multi-agent hierarchical analysis module is used to establish a distribution network multi-agent hierarchical model, in which the flexibility aggregators and microgrid system operators aggregate building flexibility resources according to user types and line locations; the constraints of the distribution network multi-agent hierarchical model include power flow constraints, distribution network line load constraints and distribution network node voltage constraints;

[0066] A building cluster user microgrid energy management analysis module is used to establish a building cluster user microgrid energy management model, wherein the optimization goal of the building cluster user microgrid energy management model is to maximize the microgrid operation benefit, and the constraints of the building cluster user microgrid energy management model include single heating area heat balance constraints, indoor temperature comfort range constraints, HVAC system constraints, power purchase price constraints, microgrid power balance constraints, power sales price constraints and MSO network operation constraints; the building cluster user microgrid energy management model is used to collaboratively manage the microgrid system operator and the building cluster users in the distribution network multi-agent hierarchical model;

[0067] A day-ahead energy management baseline scheduling optimization analysis module is used to establish a day-ahead energy management baseline scheduling optimization model, through which the optimal interactive behavior between the microgrid system operator and the building cluster users in the building cluster user microgrid energy management model is characterized, and the distribution network node load baseline and user energy consumption baseline of the distribution network-level flexibility market clearing model in the distribution network multi-agent hierarchical model are obtained;

[0068] The distribution network-level flexibility market clearing analysis module is used to establish a distribution network-level flexibility market clearing model, which includes a flexibility aggregator service declaration optimization sub-model and a distribution network operator flexibility service procurement planning sub-model; through the flexibility aggregator service declaration optimization sub-model, the distribution network node load baseline and user energy consumption baseline obtained by the day-ahead energy management baseline scheduling optimization model are used as benchmarks to optimize the flexibility service transaction plan that takes into account the differentiated interests of both flexibility aggregators and upper-level distribution network operators; through the distribution network operator flexibility service procurement planning sub-model, the distribution network operator is planned to purchase flexibility services from flexibility aggregators;

[0069] The microgrid optimization dispatch result parsing module is used to solve the distribution network-level flexibility market clearing model using the ADMM algorithm to obtain a distribution-microgrid collaborative optimization dispatch result integrating differentiated building groups.

[0070] The present invention has the following advantages: establishing a multi-agent hierarchical model of a distribution network, in which flexibility aggregators and microgrid system operators aggregate building flexibility resources according to user types and line locations; the constraints of the multi-agent hierarchical model of a distribution network include flow constraints, distribution network line load constraints and distribution network node voltage constraints; establishing a microgrid energy management model for building cluster users, the optimization goal of which is to maximize the operating benefits of the microgrid, and the constraints of the microgrid energy management model for building cluster users include single heating area thermal balance constraints, indoor temperature comfort range constraints, HVAC system constraints, power purchase price constraints, microgrid power balance constraints, power sales price constraints and MSO network operation constraints; the microgrid system operators and building cluster users in the multi-agent hierarchical model of a distribution network are collaboratively managed through the microgrid energy management model for building cluster users; establishing a baseline scheduling optimization model for day-ahead energy management, and characterizing the day-ahead energy management baseline scheduling optimization model through the baseline scheduling optimization model for day-ahead energy management The optimal interactive behavior between the microgrid system operator and the building cluster users in the building cluster user microgrid energy management model is used to obtain the distribution network node load baseline and user energy consumption baseline of the distribution network-level flexibility market clearing model in the distribution network multi-agent hierarchical model; a distribution network-level flexibility market clearing model is established, and the distribution network-level flexibility market clearing model includes a flexibility aggregator service declaration optimization submodel and a distribution network operator flexibility service procurement planning submodel; through the flexibility aggregator service declaration optimization submodel, the distribution network node load baseline and user energy consumption baseline obtained by the day-ahead energy management baseline scheduling optimization model are used as benchmarks to optimize the flexibility service transaction plan that takes into account the differentiated interests of both flexibility aggregators and upper-level distribution network operators; the distribution network operator purchases flexibility services from flexibility aggregators through the distribution network operator flexibility service procurement planning submodel; the ADMM algorithm is used to solve the distribution network-level flexibility market clearing model to obtain a distribution-micro collaborative optimization scheduling result that integrates differentiated building clusters. The present invention can realize the efficient and economical operation of the distribution network-level flexibility market; it takes into account the interests of three different entities, namely distribution system operators (DSOs), flexibility aggregators and differentiated building cluster users, adopts a bargaining strategy, and simultaneously optimizes the power and price of providing flexibility services, realizes DFM clearing, and further optimizes the congestion management of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0072] Figure 1 A schematic diagram of a DFM multi-agent collaborative architecture provided in an embodiment of the present invention;

[0073] Figure 2 A schematic diagram of a DFM operation mechanism provided in an embodiment of the present invention;

[0074] Figure 3 A schematic diagram of the DFM fund flow provided in an embodiment of the present invention;

[0075] Figure 4 A schematic diagram of a distribution-micro-cooperative optimization scheduling process for an integrated differentiated building complex provided in an embodiment of the present invention;

[0076] Figure 5 A schematic diagram of a hierarchical architecture of a distribution network provided in an embodiment of the present invention;

[0077] Figure 6 A schematic diagram of a flexibility aggregator provided in an embodiment of the present invention;

[0078] Figure 7 A flow chart of DFM clearing calculation based on the ADMM algorithm provided in an embodiment of the present invention;

[0079] Figure 8 A schematic diagram of a power distribution network provided in an embodiment of the present invention;

[0080] Fig. 9 The day-ahead forecast flexibility price curve provided in an embodiment of the present invention;

[0081] Fig.10 The load curves of lines L2 and L3 provided in an embodiment of the present invention;

[0082] Fig.11 The room temperature and HVAC power curve of a heating area at the LP3 node of the flexibility aggregator A provided in the embodiment of the present invention;

[0083] Fig.12 The voltage comparison of the LP6 node before and after the DFM is cleared provided in the embodiment of the present invention;

[0084] Fig.13 It is a DFM clearing model convergence curve provided in an embodiment of the present invention;

[0085] Fig.14 A schematic diagram of the convergence of the objective function provided in an embodiment of the present invention;

[0086] Fig.15 A schematic diagram of the architecture of a distribution-micro collaborative optimization scheduling device for an integrated differentiated building complex provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0087] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] Example 1

[0089] See also Figure 1 , which is a DFM multi-agent collaborative architecture. In DFM, DSO is an entity or organization responsible for managing and operating the distribution network, coordinating and reasonably dispatching the power resources and flexibility resources within the distribution network, and maintaining the safe, stable and economic operation of the distribution system by providing power at marginal node prices and purchasing demand-side flexibility resources; the distribution network flexibility aggregator includes flexibility aggregators and microgrid system operators (MSOs), which aggregate flexibility resources such as building users and distributed photovoltaic (PV) systems according to user types and line locations, and obtain revenue through energy transactions or flexibility transactions in the power market; among building cluster users, each building cluster contains several single buildings with the same insulation performance, with heating, ventilation and air conditioning (HVAC) equipment as typical loads, purchasing electricity from MSOs to meet electricity demand, and obtaining revenue by providing flexibility power to flexibility aggregators, participating in demand response and load regulation, and supporting congestion management of the distribution network.

[0090] Consider HVAC heating, a typical high-energy consumption equipment, as the building user load involved in regulation. The microgrid system performs day-ahead optimization scheduling for building clusters with differentiated insulation performance. By solving the microgrid energy management model of building cluster users, the MSO interacts with building cluster users to formulate a day-ahead energy consumption plan as the load baseline. HVAC equipment needs to maintain the room temperature within a comfortable range by controlling the air flow temperature at the air supply outlet. Due to the existence of building thermal inertia, the change in building room temperature lags behind the change in HVAC equipment power. Therefore, building users can flexibly adjust the energy consumption period and energy consumption within a certain range by storing heat in advance, which can provide flexibility and support the congestion management of the distribution network. Since the flexibility scale of a single user is small and the resources are scattered, it needs to be collected through flexibility aggregators to formulate flexibility service plans and participate in DFM transactions. The flexibility aggregator is the building developer. The developer and the user reach an agreement to be responsible for aggregating the flexibility resources of the contracted building cluster, and the user's HVAC equipment load is subject to its regulation.

[0091] DSO is responsible for the safe and stable operation of the distribution network and provides users with reliable electricity supply services. When the day-ahead scheduling results may cause problems such as overload and low voltage in the distribution network, DSO needs to purchase flexibility services provided by flexibility aggregators to perform congestion management. The distribution network-level flexibility market clearing model constructed by the present invention takes into account the differentiated interests of DSO, flexibility aggregators and building cluster users, and obtains a flexibility service solution that satisfies both supply and demand parties. The DFM operation process includes solving two optimization models: day-ahead energy management baseline scheduling and DFM clearing.

[0092] At the same time, considering the need for data privacy protection, users do not want flexibility aggregators to provide detailed parameters to DSOs. The present invention uses the ADMM algorithm to solve the distribution network-level flexibility market clearing model between DSOs and aggregators, and only interacts with boundary variables. After DFM clearing, DSOs pay flexibility service fees, and flexibility aggregators and building users obtain flexibility benefits.

[0093] In the embodiment of the present invention, DFM is used to consider the flexibility of building clusters to perform distribution network congestion management. In the distribution network, congestion management includes overload management and voltage management. The DFM operation mechanism is as follows: Figure 2 The details are as follows:

[0094] Firstly, the energy management of the microgrid system is carried out through the collaborative interaction between the microgrid system operator (MSO) and the building cluster users, and the baseline energy consumption plan is obtained according to the day-ahead dispatch results.

[0095] Subsequently, the DSO performs a power flow analysis on the baseline energy consumption plan to verify whether the plan meets the network operation constraints and ensure the safe and stable operation of the distribution network. If the power flow analysis results show that the day-ahead dispatch plan will cause distribution network congestion, the DSO will generate a flexibility requirement table (FRT) and broadcast it to all flexibility aggregators. The key parameters in the FRT are:

[0096] (1) The times and points where flexibility is needed;

[0097] (2) Flexibility demand: an approximate value, which provides a reference for flexibility aggregators to collect an appropriate amount of flexibility power. The actual flexibility transaction volume is determined after the DFM is cleared;

[0098] (3) Compensation percentage cap: The percentage of compensation power allowed in flexibility power, which is set by the DSO based on industry practices;

[0099] (4) Allowable compensation period: Based on the upper limit of the compensation percentage, the approximate value of the maximum compensation power can be calculated. The calculation results of the day-ahead dispatching flow are analyzed. The allowable compensation period is selected based on the principle of not causing new distribution network congestion problems, and a certain margin is considered to be retained;

[0100] (5) Flexibility price range: DSO is set based on the day-ahead forecast of the flexibility spot price.

[0101] Finally, the DFM clearing model that takes into account the interests of all parties is solved in a distributed manner using the ADMM algorithm, which can protect user privacy data while solving the distribution network congestion problem. After the DFM is cleared, the DSO pays the flexibility procurement cost, and the flexibility aggregator and building users gain benefits.

[0102] DFM fund flow Figure 3 As shown. User income consists of two parts: the income from providing flexibility and the compensation for the incremental energy cost. The user's energy cost is higher than the day-ahead dispatch baseline plan due to providing flexibility, and the flexibility aggregator should compensate for the incremental cost. The flexibility aggregator obtains flexibility service income from the DSO and pays the user a corresponding fee. Therefore, the mechanism of the DFM flexibility trading platform proposed in the present invention takes into account the interests of multiple parties and is competitive in the market. While optimizing the congestion management dispatch strategy of the distribution network, it makes both the aggregator and the user profitable.

[0103] See also Figure 4 Embodiment 1 of the present invention provides a method for optimizing the coordination and scheduling of integrated differentiated building clusters, which specifically includes the following steps:

[0104] S1. Establish a multi-agent hierarchical model of the distribution network, in which the flexibility aggregators and microgrid system operators aggregate building flexibility resources according to user types and line locations; the constraints of the multi-agent hierarchical model of the distribution network include power flow constraints, distribution network line load constraints and distribution network node voltage constraints;

[0105] S2. Establish a building cluster user microgrid energy management model, the optimization goal of which is to maximize the microgrid operation revenue, and the constraints of which include single heating area heat balance constraint, indoor temperature comfort range constraint, HVAC system constraint, power purchase price constraint, microgrid power balance constraint, power selling price constraint and MSO network operation constraint; through the building cluster user microgrid energy management model, the microgrid system operator and building cluster users in the distribution network multi-agent hierarchical model are collaboratively managed;

[0106] S3. Establish a day-ahead energy management baseline scheduling optimization model, characterize the optimal interactive behavior between the microgrid system operator and the building cluster users in the building cluster user microgrid energy management model through the day-ahead energy management baseline scheduling optimization model, and obtain the distribution network node load baseline and user energy consumption baseline of the distribution network-level flexibility market clearing model in the distribution network multi-agent hierarchical model;

[0107] S4. Establish a distribution network-level flexibility market clearing model, which includes a flexibility aggregator service declaration optimization sub-model and a distribution network operator flexibility service procurement planning sub-model; through the flexibility aggregator service declaration optimization sub-model, based on the distribution network node load baseline and user energy consumption baseline obtained by the day-ahead energy management baseline scheduling optimization model, optimize the flexibility service transaction plan that takes into account the differentiated interests of both flexibility aggregators and upper-level distribution network operators; through the distribution network operator flexibility service procurement planning sub-model, plan the distribution network operator's procurement of flexibility services from flexibility aggregators;

[0108] S5. The ADMM algorithm is used to solve the distribution network-level flexibility market clearing model to obtain a distribution-micro-coordinated optimization scheduling result integrating differentiated building complexes.

[0109] In this embodiment, a microgrid is a small power network that can operate independently or be connected to a distribution network. It usually includes power generation, energy storage, and power consumption equipment, and is managed, operated, and maintained by the MSO. Figure 5The figure shows a typical structure of a microgrid system line. Taking this line structure as an example, in step S1, the operation of the microgrid system is modeled to construct a multi-agent hierarchical model of the distribution network. The MSO needs to ensure the safe operation of the microgrid system. For any adjacent nodes m and m+1, the flow constraint formula at any time is:

[0110]

[0111] 1-ε≤V m ≤1+ε (4)

[0112] Where P m+1 is the active power injected into node m+1; P m is the active power injected into node m; Q m+1 is the reactive power injected into node m+1; Q m is the reactive power injected into node m; V m+1 is the node voltage injected into node m+1; V m is the node voltage injected into node m; is the active power consumed by the load injected into node m+1; is the reactive power consumed by the load injected into node m+1; r f 、x f are the resistance and reactance between nodes m and m+1 respectively; ε is the offset margin of the node voltage, usually set to 0.05; the node voltage takes the per-unit value.

[0113] See also Figure 6 Flexibility aggregators are a new type of entity in the power system, responsible for aggregating and managing decentralized flexibility resources, such as distributed generation, energy storage equipment and adjustable loads, thereby providing flexibility services to grid operators and participating in demand response and ancillary service market transactions, which is conducive to balancing electricity supply and demand, optimizing grid operation, and improving system stability and efficiency.

[0114] In this embodiment, in step S1, the flexibility aggregator aggregates and provides the flexibility to the distribution network in units of heating areas as follows:

[0115]

[0116] Where, subscript n is the number of the distribution network node, a is the number of the flexibility aggregator, and b is the number of the heating area; is the total amount of flexibility provided by aggregator a at node n at time t; The flexibility power provided to the HVAC equipment load of heating zone b at time t.

[0117] Among them, DSO needs to maintain the safe and stable operation of the distribution system. The distribution network line load constraint formula of the distribution network multi-agent hierarchical model is:

[0118]

[0119] |F l,t |≤F l max (7)

[0120] Where P n,t is the active load power at node n at time t; G n,l is the element in the nth row and lth column of the distribution network correlation matrix; F l max is the upper limit of the rated load of line l; F l,t is the load flowing through line l at time t;

[0121] The distribution network node voltage constraint formula of the distribution network multi-agent hierarchical model is:

[0122]

[0123]

[0124] In the formula, Q n,t is the reactive load power at node n at time t; is the power factor of node n; V n,t is the node voltage of node n at time t; V s is the bus voltage on the low voltage side of the substation; is the impedance of the line between nodes n and n*; V min is the lower limit of the node voltage amplitude; j is the imaginary unit is the active load power of nodes except node n; is the reactive load power except node n.

[0125] In this embodiment, in step S2, the heat balance constraints of the single heating area are respectively listed for the wall hot spots and the indoor air hot spots according to the law of conservation of energy. The expression formula of the heat balance constraints of the single heating area of ​​the building cluster user microgrid energy management model is:

[0126]

[0127] In the formula, the subscript ag is the building cluster number; are the heat capacity, midpoint temperature and thermal resistance of the wall between the heating area and air node j respectively; α 1,j , are the heat absorption rate, surface area and light intensity of the wall respectively; r 1, j is a binary variable, which takes the value 1 when the wall is illuminated and 0 otherwise; τ win are the thermal resistance, area and transmittance of the window respectively; is the temperature of air node j; is the temperature of the indoor air in the heating area; c air 、m r , They are indoor air heat capacity, air specific heat capacity, HVAC supply air flow rate and supply air temperature in the heating area; They are random heat gain of indoor air and light radiation heat gain in the heating area respectively; j=1,2,3,4,5.

[0128] In this embodiment, in step S2, the indoor temperature comfort range constraint of the building cluster user microgrid energy management model is expressed as:

[0129]

[0130] In the formula, subscript s is the heating zone number, and t is the time number; is the temperature of the indoor air in the heating area; T r ,min , T r,max are the lower and upper limits of the indoor temperature comfort range respectively;

[0131] Step S2, the expression formula of the HVAC system constraint of the building cluster user microgrid energy management model is:

[0132]

[0133] In the formula, is the HVAC supply air temperature; T t h,min , T t h,max are the lower limit and upper limit of the HVAC supply air outlet air temperature at time t, respectively; are the power purchased from the microgrid by the single heating area at time t, the power consumed by HVAC, and the power consumed by uncontrollable electrical equipment; m r is the HVAC supply air flow; c air is the specific heat capacity of air; C COP is the energy efficiency ratio of the HVAC equipment;

[0134] In step S2, the expression formula of the power purchase price constraint of the building cluster user microgrid energy management model is:

[0135]

[0136] In the formula, is the electricity price sold by the microgrid to users at time t; are the lower and upper limits of the microgrid electricity price at time t; C sale,avg is the upper limit of the average electricity selling price of the microgrid; T is the number of control cycles in a day;

[0137] In step S2, the expression formula of the building cluster user optimization target of the building cluster user microgrid energy management model is:

[0138]

[0139] In the formula, OC ag is the energy cost of the users in the lower building cluster ag; Δt is the duration of a single control cycle; is the electricity purchase price of the user at time t; is the power purchased by the single heating area from the microgrid at time t.

[0140] In step S2, the microgrid power balance constraint of the building cluster user microgrid energy management model is:

[0141]

[0142] Where P t , P t pv and They are the power purchased by the microgrid from the upper distribution network at time t, the total output of the distributed PV system in the microgrid, and the power sold by the microgrid to the single heating area;

[0143] In step S2, the power selling price constraint formula of the building cluster user microgrid energy management model is:

[0144]

[0145] In the formula, is the electricity price sold by the microgrid to users at time t;

[0146] In step S2, the power flow network is a unidirectional single-branch structure. For any adjacent nodes m and m+1, the power flow constraints at any time are shown in equations (22) and (23), and the node voltage constraints are shown in equations (24) and (25):

[0147]

[0148] 1-ε≤V m ≤1+ε (25)

[0149] Where P m+1 is the active power injected into node m+1; P m is the active power injected into node m; Q m+1 is the reactive power injected into node m+1; Qm is the reactive power injected into node m; V m+1 is the node voltage injected into node m+1; V m is the node voltage injected into node m; is the active power consumed by the load injected into node m+1; is the reactive power consumed by the load injected into node m+1; r f 、x f are the resistance and reactance between nodes m and m+1 respectively; ε is the offset margin of the node voltage, usually set to 0.05; the node voltage takes the per-unit value.

[0150] In step S2, the MSO optimization objective formula of the building cluster user microgrid energy management model is:

[0151]

[0152] In the formula, OM is the income of the upper-level MSO, C t is the price of electricity purchased by the microgrid system from the upper distribution network at time t; is the electricity price sold by the microgrid to users at time t; P is the power sold by the microgrid to the single heating area at time t; t is the power purchased by the microgrid from the distribution network at time t.

[0153] In this embodiment, in step S3, the optimal interactive behavior between the microgrid system operator and the building cluster users in the building cluster user microgrid energy management model is characterized by the day-ahead energy management baseline scheduling optimization model, and the distribution network node load baseline and the user energy baseline of the distribution network-level flexibility market clearing model in the distribution network multi-agent hierarchical model are obtained as follows:

[0154]

[0155]

[0156] In the formula, is the day-ahead load dispatch baseline power at node n at time t; is the HVAC equipment load power of heating area b at time t in the day-ahead dispatch baseline plan; is the price at which the users of the building cluster aggregated by aggregator a at node n in the baseline scenario of day-ahead dispatch purchase electricity from the microgrid system operator MSO at time t. Among them, the same type of building clusters under the microgrid system connected to different nodes are constructed by the same building developer, that is, the same flexibility aggregator is responsible for aggregating their flexibility resources.

[0157] In this embodiment, during the DFM clearing process, the upper-level DSO hopes to reduce the procurement cost of flexibility services as much as possible to improve economic efficiency, while the lower-level flexibility aggregator seeks to maximize the profit of providing flexibility services, and there is a conflict of interest goals. Therefore, the present invention constructs a distribution network-level flexibility market clearing model that takes into account the differentiated interest demands of both the supply and demand sides of flexibility services, while optimizing flexibility power and price, and providing a solution for formulating a flexibility trading plan that satisfies both upper and lower-level market entities.

[0158] In the distribution network-level flexibility market clearing model, the flexibility aggregator service application optimization submodel provides flexibility services by secondary regulating the HVAC equipment load of building users based on the user's energy consumption day-ahead dispatch baseline and FRT. The objective function formula of the flexibility aggregator service application optimization submodel is:

[0159]

[0160] In the formula, OA a The revenue for flexibility aggregators to provide flexibility services to the distribution network; For the set of nodes that require flexibility; is the set of heating areas of the building cluster aggregated by aggregator a under the microgrid system connected to node n; is the price at which aggregator a provides flexibility at node n at time t; is the total amount of flexibility provided by aggregator a at node n at time t; is the flexibility cost coefficient of heating zone b at time t; The flexibility power provided to the HVAC equipment load in heating zone b at time t; It is the incremental energy cost of heating area b due to providing flexibility at time t.

[0161] Among them, in step S4, the constraints of the flexibility aggregator service application optimization sub-model include flexibility aggregation constraints, compensation power constraints, HVAC equipment load constraints, flexibility selling price constraints, user energy cost incremental compensation constraints and user basic energy demand constraints.

[0162] Specifically, the flexibility aggregation constraint formula of the flexibility aggregator service application optimization sub-model is:

[0163]

[0164] In the formula, subscript b is the heating area number; The flexibility power provided to the HVAC equipment load of heating zone b at time t, against Time period, against Time period.

[0165] Specifically, the compensation power constraint formula of the flexibility aggregator service application optimization sub-model is as follows:

[0166]

[0167] In the formula, is the total amount of compensation power required by aggregator a at node n at time t; is the compensation power required by the HVAC equipment load in heating area b at time t; α is the upper limit of the compensation percentage.

[0168] Specifically, the HVAC equipment load constraint formula of the flexibility aggregator service application optimization sub-model is as follows:

[0169]

[0170] In the formula, is the electrical power consumed by the HVAC equipment in heating zone b at time t; is the HVAC equipment load power of heating area b at time t in the day-ahead dispatch baseline plan.

[0171] Specifically, the flexibility price constraint formula of the flexibility aggregator service application optimization sub-model is as follows:

[0172]

[0173] In the formula, and are the lower and upper limits of the flexibility price respectively.

[0174] Specifically, the user energy cost incremental compensation constraint formula of the flexibility aggregator service application optimization sub-model is as follows:

[0175]

[0176] In the formula, is the price at which the building cluster users aggregated by aggregator a at node n purchase electricity from the microgrid system operator (MSO) at time t in the day-ahead dispatch baseline scheme.

[0177] Specifically, the premise for users to be willing to provide flexibility is to meet basic energy demand. Therefore, while flexibly adjusting the HVAC equipment load, the indoor temperature needs to be maintained within the comfort range. The basic energy demand constraint formula of the user in the flexibility aggregator service application optimization sub-model is as follows:

[0178]

[0179] In the formula, is the electric power consumed by HVAC at time t; C COP is the energy efficiency ratio of the HVAC equipment, is the midpoint temperature of the wall between the heating area and air node j at time t; is the temperature of air node j at time t; is the indoor air temperature of the heating area at time t; is the indoor HVAC supply air temperature of the heating area at time t; and They are the random heat gain of indoor air and the heat gain of light radiation in the heating area at time t.

[0180] In this embodiment, in step S4, the DSO purchases flexibility services from the flexibility aggregator to solve the distribution network congestion problem. The objective function formula of the distribution network operator flexibility service procurement planning sub-model is:

[0181]

[0182] Where OD is the cost of purchasing flexibility services by the distribution system operator DSO; are the price and power of flexibility purchased by DSO from aggregator a at time t; N ag is the aggregation business set; is the set of blocking moments of node n.

[0183] Among them, the constraints of the distribution network operator flexibility service procurement planning sub-model include node load power constraints, flexibility procurement price constraints and distribution network operation constraints.

[0184] Specifically, the node load power constraint formula of the distribution network operator flexibility service procurement planning sub-model is as follows:

[0185]

[0186] In the formula, is the total amount of compensation power provided by DSO to aggregator a at node n at time t, For node range For node range

[0187] Specifically, DSO sets the flexibility price range based on the day-ahead forecast flexibility spot price and reflects it in FRT. The flexibility procurement price constraint formula of the distribution network operator's flexibility service procurement planning sub-model is as follows:

[0188]

[0189] in,

[0190] Specifically, DSO is responsible for maintaining the safe and stable operation of the distribution network at all times and nodes within the radiation range. In addition to solving the original congestion problem, the DFM clearing process also needs to ensure that no new congestion occurs at other times and nodes. The distribution network operation constraint formula of the distribution network operator flexibility service procurement planning sub-model is the same as the distribution network line load constraint and distribution network node voltage constraint formula in the distribution network multi-agent hierarchical model.

[0191] In this embodiment, in step S5, the steps of using the ADMM algorithm to solve the distribution network-level flexibility market clearing model to obtain a distribution-micro-cooperative optimization scheduling result integrating differentiated building groups include:

[0192] Set initial values ​​for global variables, dual variables, residuals, dual residuals, and penalty factors;

[0193] Solve the DSO optimization subproblem and the flexibility aggregator optimization subproblem;

[0194] Update global variables, dual variables, residuals, dual residuals, and penalty factors;

[0195] Check whether both the residual and the dual residual meet the convergence judgment conditions. If so, stop the iteration and output the results. If not, re-optimize the distribution system operator DSO subproblem and the flexibility aggregator subproblem.

[0196] See also Figure 7 , since DFM clearing will obtain a flexibility service transaction plan based on the consensus of the upper-layer DSO and the lower-layer flexibility aggregator, the supply and demand sides need to unify the flexibility price, flexibility power and compensation power, so a global consistency constraint is established:

[0197]

[0198] Create the augmented Lagrangian function:

[0199]

[0200] Where μ is the set of dual variables corresponding to the coupling constraint; ρ is the penalty factor.

[0201] Among them, the upper sub-problem is the optimization model of the distribution network operator, and the objective function of the upper sub-problem is:

[0202]

[0203] The constraints of the upper subproblem are:

[0204] st formula (6) to formula (9), formula (47) to formula (49).

[0205] The lower level sub-problem is the flexibility aggregator optimization problem, and the objective function of the lower level sub-problem is:

[0206] The constraints of the lower-level subproblem are: st equation (22) to equation (35).

[0207] Specifically, the solution process of the distribution network-level flexibility market clearing model is as follows:

[0208] 1) Transformation of the model to a convex problem

[0209] The McCormick envelope method is used to linearly relax the nonlinear part.

[0210] (a) Upper-level subproblem - DSO optimization model

[0211] Linearly relax the nonlinear terms in the objective function of the upper subproblem:

[0212]

[0213] Where: is a nonlinear term 's substitution variable.

[0214] Then the new objective function is:

[0215]

[0216] The linear relaxation supplementary constraint is:

[0217]

[0218] Where: f min and f max The lower and upper bounds of the flexibility power provided to a node by the aggregator are given respectively.

[0219] Then the constraints of the upper sub-problem are transformed into:

[0220] st Formula (40) to Formula (43), Formula (37) to Formula (39), Formula (52) to Formula (55)

[0221] (b) Lower-level sub-problem - flexibility aggregator optimization model

[0222] Similarly, the nonlinear term in the objective function of the lower-level sub-problem is linearly relaxed, as shown in formula (62):

[0223]

[0224] Where: is a nonlinear term 's substitution variable.

[0225] The new objective function of the lower layer is shown in formula (63):

[0226]

[0227] The linear relaxation supplementary constraints are shown in equations (64) to (67):

[0228]

[0229] Then the constraints of the upper sub-problem are transformed into:

[0230] st is formula (5), formula (31) to formula (45), formula (64) to formula (67)

[0231] (c) Solution process:

[0232] After linear relaxation using McCormick envelope method, both the upper and lower sub-problems are transformed into convex problems. The DFM clearing model is solved hierarchically and distributedly according to the ADMM algorithm. The steps are as follows.

[0233] (1) Parameter initialization

[0234] The number of iterations k is set to 0. Reasonably set the initial values ​​of the coupling variables, dual variables, residuals, dual residuals, and penalty factors.

[0235] (2) Solve subproblems alternately

[0236] The upper and lower subproblems are solved by DSO and flexibility aggregator respectively.

[0237] Upper subproblem: The objective function is equation (57); the constraints are equations (6) to (9), (47) to (49), and (58) to (61).

[0238] Lower-level subproblem: The objective function is equation (63); the constraints are equation (5), equations (31) to (45), and equations (64) to (67).

[0239] Solve the sub-problems in turn and obtain the values ​​of each optimization variable for k+1 iterations.

[0240] (3) Update parameters

[0241] The dual variables are updated according to equations (68) to (70):

[0242]

[0243] The residual and dual residual are updated according to equations (71) to (76):

[0244]

[0245] Where: R is the residual set, S is the dual residual set.

[0246] In order to speed up the convergence of the ADMM algorithm, the penalty factor is dynamically updated according to formula (77) in each iteration:

[0247]

[0248] Where: τ and ζ are update parameters.

[0249] (4) Determine the convergence conditions

[0250] Check whether the residual and the dual residual satisfy the convergence judgment conditions, as shown in equations (78) and (79):

[0251] ||R (k+1) ||2≤σ R (78)

[0252] ||S (k+1) ||2≤σ S (79)

[0253] Where: R and σ S are the convergence margins of the residual and the dual residual, respectively.

[0254] If both the residual and the dual residual have converged to within the margin range, the algorithm is considered to have converged, the iteration is stopped, and the DFM clearing result is output. Otherwise, set k = k + 1 and return to step 2 for the next iteration.

[0255] The example of the embodiment of the present invention is set as follows:

[0256] Distribution network Figure 8As shown, two feeders are considered, feeder 1 includes lines L1-L5 and load nodes LP1-LP3, and feeder 2 includes lines L6-L12 and load nodes LP4-LP7. The line impedance is 0.26+j0.027Ω / km. The upper limit of the rated load of the line L1-L12 is shown in Table 1. The node voltage is expressed in per-unit value, and the lower limit is set to 0.95pu. Each microgrid system contains three building clusters with different insulation performance, and the insulation performance is ranked from high to low as A>B>C. Each building cluster has 200 heating areas of HVAC equipment participating in load regulation. The present invention considers that the same type of building clusters of different microgrid systems are constructed by the same developer, and the flexibility load resources of the building users are integrated by the developer to which they belong, and all developers enter the energy industry to become flexibility aggregators. Therefore, the DFM in this scenario introduces three flexibility aggregators A, B, and C. One aggregator has 200 heating areas at each load node, and aggregating the flexibility load resources of 1,400 heating areas. The current forecast flexibility spot price is as follows Fig. 9 Analyze the user's willingness to provide flexibility and reasonably set the building user flexibility cost coefficient And it obeys normal distribution. The price range of flexibility service is within 5 times of the predicted price. Light intensity, outdoor temperature, and distributed photovoltaic (PV) system output.

[0257] Table 1 Upper limit of line rated load

[0258]

[0259] Among them, the MSO submits the energy consumption plan to the DSO, and the DSO performs power flow analysis on the energy consumption baseline data reported by each load node to determine whether the line load and node voltage exceed the safety range and obtain the day-ahead flexibility demand. Fig.10 As shown in Table 2, line L2 has an overload of 20.6kW and 6.4kW at 08:00 and 20:45 respectively, and line L3 has an overload of 28kW at 08:00. The voltage at node LP6 is lower than the lower limit. In order to solve the above problems, DSO needs to purchase flexibility services in DFM to achieve congestion management. Through power flow analysis and node voltage sensitivity analysis, DSO obtains the FRT shown in Table 2.

[0260] Table 2 Flexibility requirements

[0261]

[0262] Among them, DFM clears according to the results of the day-ahead flexibility demand analysis and obtains the flexibility service transaction plan, as shown in Table 3. It can be seen from the table that at the same time and the same node, the number of flexibility provided by the aggregator is ranked from high to low as A>B>C. This is because the insulation performance of the Class A building cluster belonging to aggregator A is better, followed by B, and C is the worst. Buildings with better insulation performance have greater thermal inertia and require less heat storage to maintain room temperature within a comfortable range, so they can provide more flexibility at overload moments. In addition, the indoor temperature changes of building clusters with better insulation performance are more delayed than those of electrical quantities, so that the heat lost by providing flexibility can be replenished in the allowed compensation period after a certain period of time.

[0263] Table 3 Overload management flexibility service clearing results

[0264]

[0265] Taking into account the differentiated interests of flexibility aggregators and DSOs, the optimization results of flexibility service plans and prices are an acceptable balance of interests for both parties. It can be seen that the price of flexibility services provided by aggregators to DSOs at 08:00 is significantly higher than that at 20:45. The power of HVAC equipment at 08:00 is greater than that at 20:45, and building clusters tend to store heat at around 08:00. Therefore, users are less willing to increase flexibility at this time, and the cost coefficient is In order to ensure that the benefits of the flexibility aggregator meet its interests, the optimization result of the flexibility price at this moment is higher.

[0266] From the changes in the load conditions of lines L2 and L3, it can be seen that the overload problem of L2 and L3 at the time of congestion is solved, and no new overload is caused during the compensation period, which verifies the effectiveness of overload management through DFM clearing.

[0267] Taking a heating area of ​​a Class A building cluster at the LP3 node as an example, the HVAC load curve and indoor temperature curve of the heating area are as follows: Fig.11 User preferences, baseline room temperature, and building insulation performance all influence the amount of flexibility provided by users. Fig.11 It can be clearly seen that the room temperature change caused by the thermal inertia of the building lags behind the change in electrical quantity. As can be seen from the figure, after the HVAC equipment load is adjusted to provide flexibility, the indoor temperature is still maintained within the comfort range, which shows that the proposed DFM mechanism is reasonable and feasible. According to the DFM clearing results, users will rearrange their day-ahead energy consumption plans. During the period of allowed compensation, the user load will quickly return to the baseline value after the necessary power compensation, thereby minimizing the cost increment caused by deviation from the baseline energy consumption.

[0268] To manage voltage, the aggregator provides flexibility to the distribution network at nodes LP4, LP5, and LP7 to solve the problem of low voltage at the node LP6. The DFM clearing results are shown in Table 4. As can be seen from the table, the number of flexibility services provided by the aggregator is still ranked from high to low as A>B>C, and for any aggregator, the flexibility provided for voltage management is the largest at node LP4, followed by LP5, and the least at LP7. This is because the greater the line impedance between the node providing flexibility and the undervoltage node, the higher the sensitivity of the undervoltage node voltage to flexibility, and the better the voltage management effect when the same amount of flexibility is provided. The line impedance is proportional to the line length, so in order to minimize the cost of purchasing flexibility services, the DSO will be more inclined to use the flexibility provided by the node farther away from the undervoltage node on the same feeder.

[0269] Table 4 Voltage management flexibility service clearing results

[0270]

[0271] Among them, the change of the node voltage of node LP6 before and after voltage management is as follows: Fig.12 As shown in the figure, the node voltage of LP6 at 08:00 returns to the safe range, and the voltage is higher than the lower limit within the time range of the day-ahead scheduling. Therefore, it is effective to use DFM to solve the node low voltage problem.

[0272] The DFM clearing problem is solved iteratively using the DMM algorithm. In the example set by the present invention, the convergence margin of the residual and the dual residual is 0.001, the initial value of the penalty factor is 10, and the update parameters ζ and τ are 20 and 5 respectively. The convergence of the DFM clearing problem is shown in Fig.13 As shown in Figure 2, after 284 iterations, both the residual and the dual residual dropped below the convergence margin, and the model converged.

[0273] In order to illustrate the accuracy of the optimization results of the proposed hierarchical distributed algorithm, the DFM clearing mechanism is represented by a centralized model. A master-slave game architecture is adopted, with DSO as the upper leader and flexibility aggregator as the lower follower. The flexibility aggregator optimization model is then transformed into the constraints of the upper model through the Karush-Kuhn-Tucker condition, and the original two-level optimization problem is transformed into a single-level problem, and the CPLEX solver is used for optimization calculation.

[0274] The objective function value iteration process of the distributed model is compared with the optimization result of the objective function of the centralized model. Fig.14As shown in the figure, it can be seen that the objective function of the ADMM algorithm eventually converges to the objective function value of the centralized algorithm optimization result, and solving the distributed model can obtain the optimal solution consistent with the centralized one. The DSO procurement cost and flexibility service benefits of the flexibility aggregator obtained by the two algorithm models are shown in Table 5. This verifies the accuracy of the hierarchical distributed algorithm constructed by the present invention.

[0275] Table 5 Comparison of objective function optimization results between distributed model and centralized model

[0276]

[0277] Among them, the calculation time of the distributed algorithm and the centralized algorithm is 994 seconds and 47 seconds respectively. The distributed model solution process based on the ADMM algorithm requires more calculation time than the centralized model. Since the DFM clearing is carried out on the day before, the calculation time of more than ten minutes is not a serious problem and is acceptable. Considering the advantages of distributed algorithms in privacy data protection, the hierarchical distributed DFM mechanism constructed by the present invention has broad practical value and broad application prospects.

[0278] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0279] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0280] Example 2

[0281] See also Fig.15 The embodiment of the present invention further provides a distribution-micro-cooperative optimization scheduling device for an integrated differentiated building complex, and adopts a distribution-micro-cooperative optimization scheduling method for an integrated differentiated building complex of the above embodiment, including:

[0282] The distribution network multi-agent hierarchical analysis module 100 is used to establish a distribution network multi-agent hierarchical model, in which the flexibility aggregators and microgrid system operators aggregate building flexibility resources according to user types and line locations; the constraints of the distribution network multi-agent hierarchical model include power flow constraints, distribution network line load constraints and distribution network node voltage constraints;

[0283] The building cluster user microgrid energy management analysis module 200 is used to establish a building cluster user microgrid energy management model, wherein the optimization goal of the building cluster user microgrid energy management model is to maximize the microgrid operation benefit, and the constraints of the building cluster user microgrid energy management model include single heating area heat balance constraints, indoor temperature comfort range constraints, HVAC system constraints, power purchase price constraints, microgrid power balance constraints, power sales price constraints and MSO network operation constraints; the building cluster user microgrid energy management model is used to collaboratively manage the microgrid system operator and the building cluster users in the distribution network multi-agent hierarchical model;

[0284] The day-ahead energy management baseline scheduling optimization analysis module 300 is used to establish a day-ahead energy management baseline scheduling optimization model, and to characterize the optimal interactive behavior between the microgrid system operator and the building cluster users in the building cluster user microgrid energy management model through the day-ahead energy management baseline scheduling optimization model, so as to obtain the distribution network node load baseline and the user energy consumption baseline of the distribution network-level flexibility market clearing model in the distribution network multi-agent hierarchical model;

[0285] The distribution network-level flexibility market clearing analysis module 400 is used to establish a distribution network-level flexibility market clearing model, which includes a flexibility aggregator service declaration optimization sub-model and a distribution network operator flexibility service procurement planning sub-model; through the flexibility aggregator service declaration optimization sub-model, the distribution network node load baseline and the user energy consumption baseline obtained by the day-ahead energy management baseline scheduling optimization model are used as benchmarks to optimize the flexibility service transaction plan that takes into account the differentiated interests of both the flexibility aggregator and the upper-level distribution network operator; through the distribution network operator flexibility service procurement planning sub-model, the distribution network operator is planned to purchase flexibility services from the flexibility aggregator;

[0286] The microgrid optimization dispatch result analysis module 500 is used to solve the distribution network-level flexibility market clearing model using the ADMM algorithm to obtain a distribution-microgrid collaborative optimization dispatch result of an integrated differentiated building complex.

[0287] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned device are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown in the previous part of the present application, and will not be repeated here.

[0288] Example 3

[0289] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a distribution-micro collaborative optimization scheduling method for an integrated differentiated building complex is stored. The program code includes instructions for executing embodiment 1 or any possible implementation method of a distribution-micro collaborative optimization scheduling method for an integrated differentiated building complex.

[0290] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0291] Example 4

[0292] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0293] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a distribution-micro collaborative optimization scheduling method for an integrated differentiated building complex of embodiment 1 or any possible implementation thereof.

[0294] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0295] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0296] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0297] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A distribution-micro-coordinated optimization scheduling method for integrated differentiated building groups, characterized in that: include: Establishing a multi-agent hierarchical model of a distribution network, in which flexibility aggregators and microgrid system operators aggregate building flexibility resources according to user types and line locations; the constraints of the multi-agent hierarchical model of a distribution network include power flow constraints, distribution network line load constraints, and distribution network node voltage constraints; Establish a building cluster user microgrid energy management model, the optimization goal of the building cluster user microgrid energy management model is to maximize the microgrid operation income, and the constraints of the building cluster user microgrid energy management model include single heating area heat balance constraints, indoor temperature comfort range constraints, HVAC system constraints, power purchase price constraints, microgrid power balance constraints, power sales price constraints and MSO network operation constraints; through the building cluster user microgrid energy management model, the microgrid system operator and the building cluster users in the distribution network multi-agent hierarchical model are collaboratively managed; Establish a day-ahead energy management baseline scheduling optimization model, characterize the optimal interactive behavior between the microgrid system operator and the building cluster users in the building cluster user microgrid energy management model through the day-ahead energy management baseline scheduling optimization model, and obtain the distribution network node load baseline and user energy consumption baseline of the distribution network-level flexibility market clearing model in the distribution network multi-agent hierarchical model; Establishing a distribution network-level flexibility market clearing model, wherein the distribution network-level flexibility market clearing model includes a flexibility aggregator service declaration optimization sub-model and a distribution network operator flexibility service procurement planning sub-model; Through the flexibility aggregator service application optimization sub-model, the distribution network node load baseline and user energy baseline obtained by the day-ahead energy management baseline scheduling optimization model are used as the benchmark to optimize the flexibility service transaction plan that takes into account the differentiated interests of both the flexibility aggregator and the upper-level distribution network operator; Planning the distribution network operator's purchase of flexibility services from the flexibility aggregator through the distribution network operator's flexibility service procurement planning sub-model; The ADMM algorithm is used to solve the distribution network-level flexibility market clearing model, and a distribution-micro-cooperative optimization dispatch result integrating differentiated building groups is obtained; The optimal interactive behavior between the microgrid system operator and the building cluster users in the building cluster user microgrid energy management model is characterized by the day-ahead energy management baseline scheduling optimization model, and the formulas for the distribution network node load baseline and the user energy baseline of the distribution network-level flexibility market clearing model in the distribution network multi-agent hierarchical model are obtained as follows: In the formula, is the day-ahead load dispatch baseline power at node n at time t; is the HVAC equipment load power of heating area b at time t in the day-ahead dispatch baseline plan; is the price at which the building cluster users aggregated by aggregator a at node n purchase electricity from the microgrid system operator MSO at time t in the baseline plan for day-ahead dispatch; t The power purchased by the microgrid from the upper distribution network at time t; is the electrical power consumed by HVAC at time t; is the electricity price sold by the microgrid to users at time t; is the electricity purchase price of the user at time t.

2. According to claim 1, a distribution-micro-coordinated optimization scheduling method for integrated differentiated building complexes is characterized in that: The power flow constraint formula of the multi-agent hierarchical model of the distribution network is: 1-ε≤V m ≤1+ε Where P m+1 is the active power injected into node m+1; P m is the active power injected into node m; Q m+1 is the reactive power injected into node m+1; Q m is the reactive power injected into node m; V m+1 is the node voltage injected into node m+1; V m is the node voltage injected into node m; is the active power consumed by the load injected into node m+1; is the reactive power consumed by the load injected into node m+1; r f 、x f are the resistance and reactance between nodes m and m+1 respectively; ε is the offset margin of the node voltage.

3. The method for optimizing the distribution and micro-coordination of integrated differentiated building complexes according to claim 2 is characterized in that: The distribution network line load constraint formula of the distribution network multi-agent hierarchical model is: |F l,t |≤F l max Where P n,t is the active load power at node n at time t; G n,l is the element in the nth row and lth column of the distribution network correlation matrix; F l max is the upper limit of rated load of line l; F l,t is the load flowing through line l at time t; The distribution network node voltage constraint formula of the distribution network multi-agent hierarchical model is: In the formula, Q n,t is the reactive load power at node n at time t; is the power angle of node n; V n,t is the node voltage of node n at time t; V s is the bus voltage on the low voltage side of the substation; is the impedance of the line between nodes n and n*; V min is the lower limit of the node voltage amplitude; j is the imaginary unit is the active load power of nodes except node n; is the reactive load power except node n.

4. The method for optimizing the distribution and micro-coordination of integrated differentiated building complexes according to claim 3 is characterized in that: The expression formula of the heat balance constraint of the single heating area of ​​the building cluster user microgrid energy management model is: In the formula, the subscript ag is the building cluster number; are the heat capacity, midpoint temperature and thermal resistance of the wall between the heating area and air node j respectively; α 1,j , are the heat absorption rate, surface area and light intensity of the wall respectively; r 1,j It is a binary variable, which takes the value of 1 when the wall is exposed to light and 0 otherwise; τ win are the thermal resistance, area and transmittance of the window respectively; is the temperature of air node j; is the temperature of the indoor air in the heating area; c air 、m r , They are indoor air heat capacity, air specific heat capacity, HVAC supply air flow rate and supply air temperature in the heating area; They are random heat gain of indoor air and light radiation heat gain in the heating area respectively; j=1,2,3,4,5.

5. The method for optimizing the dispatching of a distributed and micro-scale integrated differentiated building complex according to claim 4 is characterized in that: The expression formula of the indoor temperature comfort range constraint of the building cluster user microgrid energy management model is: In the formula, subscript s is the heating zone number, and t is the time number; is the temperature of the indoor air in the heating area; T r,min , T r ,max are the lower and upper limits of the indoor temperature comfort range respectively; The expression formula of the HVAC system constraint of the building cluster user microgrid energy management model is: In the formula, is the HVAC supply air temperature; T t h,min , T t h,max are the lower limit and upper limit of the HVAC supply air outlet air temperature at time t, respectively; are the power purchased from the microgrid by the single heating area at time t and the power consumed by the uncontrollable electrical equipment; m r is the HVAC supply air flow; c air is the specific heat capacity of air; C COP is the energy efficiency ratio of the HVAC equipment; The expression formula of the power purchase price constraint of the building cluster user microgrid energy management model is: In the formula, is the electricity price sold by the microgrid to users at time t; are the lower and upper limits of the microgrid electricity price at time t; C sale,avg The upper limit of the average electricity selling price of the microgrid; T is the number of control cycles in a day; The MSO network operation constraints of the building cluster user microgrid energy management model are the same as the power flow constraints of the distribution network multi-agent hierarchical model; The expression formula of the building cluster user optimization target of the building cluster user microgrid energy management model is: In the formula, OC ag is the energy cost of the users in the lower building cluster ag; Δt is the duration of a single control cycle; is the electricity purchase price of the user at time t; is the power purchased by the single heating area from the microgrid at time t The microgrid power balance constraint of the building cluster user microgrid energy management model is: Where P t pv is the total output of the distributed PV system in the microgrid at time t; The power selling price constraint formula of the building cluster user microgrid energy management model is: In the formula, is the electricity price sold by the microgrid to users at time t; The MSO optimization objective formula of the building cluster user microgrid energy management model is: In the formula, OM is the income of the upper-level MSO, C t is the price of electricity purchased by the microgrid system from the upper distribution network at time t; is the electricity price sold by the microgrid to users at time t; P is the power sold by the microgrid to the single heating area at time t; t is the power purchased by the microgrid from the distribution network at time t.

6. The method for optimizing the distribution and micro-coordination of integrated differentiated building complexes according to claim 5 is characterized in that: The objective function formula of the flexibility aggregator service declaration optimization sub-model is: In the formula, OA a The revenue for flexibility aggregators to provide flexibility services to the distribution network; For the set of nodes that require flexibility; is the set of heating areas of the building cluster aggregated by aggregator a under the microgrid system connected to node n; is the price at which aggregator a provides flexibility at node n at time t; is the total amount of flexibility provided by aggregator a at node n at time t; is the flexibility cost coefficient of heating zone b at time t; The flexibility power provided to the HVAC equipment load in heating zone b at time t; The incremental energy cost of heating area b due to providing flexibility at time t; The constraints of the flexibility aggregator service application optimization sub-model include flexibility aggregation constraints, compensation power constraints, HVAC equipment load constraints, flexibility selling price constraints, user energy cost incremental compensation constraints and user basic energy demand constraints.

7. The method for optimizing the distribution and micro-coordination of integrated differentiated building complexes according to claim 6 is characterized in that: The objective function formula of the distribution network operator flexibility service procurement planning sub-model is: Where OD is the cost of purchasing flexibility services by the distribution system operator DSO; are the price and power of flexibility purchased by DSO from aggregator a at time t; N ag is the aggregation business set; is the blocking time set of node n; The constraints of the distribution network operator flexibility service procurement planning sub-model include node load power constraints, flexibility procurement price constraints and distribution network operation constraints.

8. The method for optimizing the dispatching of a distributed and micro-scale integrated differentiated building complex according to claim 7 is characterized in that: The steps of solving the distribution network-level flexibility market clearing model by using the ADMM algorithm to obtain a distribution-micro-cooperative optimization dispatch result integrating differentiated building groups include: Set initial values ​​for global variables, dual variables, residuals, dual residuals, and penalty factors; Solve the DSO optimization subproblem and the flexibility aggregator optimization subproblem; Update global variables, dual variables, residuals, dual residuals, and penalty factors; Check whether both the residual and the dual residual meet the convergence judgment conditions. If so, stop the iteration and output the results. If not, re-optimize the distribution system operator DSO subproblem and the flexibility aggregator subproblem.

9. A distribution-micro-cooperative optimization scheduling device for an integrated differentiated building complex, using a distribution-micro-cooperative optimization scheduling method for an integrated differentiated building complex as claimed in any one of claims 1 to 8, characterized in that: include: A distribution network multi-agent hierarchical analysis module is used to establish a distribution network multi-agent hierarchical model, in which the flexibility aggregators and microgrid system operators aggregate building flexibility resources according to user types and line locations; the constraints of the distribution network multi-agent hierarchical model include power flow constraints, distribution network line load constraints and distribution network node voltage constraints; A building cluster user microgrid energy management analysis module is used to establish a building cluster user microgrid energy management model, wherein the optimization goal of the building cluster user microgrid energy management model is to maximize the microgrid operation benefit, and the constraints of the building cluster user microgrid energy management model include single heating area heat balance constraints, indoor temperature comfort range constraints, HVAC system constraints, power purchase price constraints, microgrid power balance constraints, power sales price constraints and MSO network operation constraints; the building cluster user microgrid energy management model is used to collaboratively manage the microgrid system operator and the building cluster users in the distribution network multi-agent hierarchical model; A day-ahead energy management baseline scheduling optimization analysis module is used to establish a day-ahead energy management baseline scheduling optimization model, through which the optimal interactive behavior between the microgrid system operator and the building cluster users in the building cluster user microgrid energy management model is characterized, and the distribution network node load baseline and user energy consumption baseline of the distribution network-level flexibility market clearing model in the distribution network multi-agent hierarchical model are obtained; A distribution network-level flexibility market clearing analysis module, used to establish a distribution network-level flexibility market clearing model, wherein the distribution network-level flexibility market clearing model includes a flexibility aggregator service declaration optimization sub-model and a distribution network operator flexibility service procurement planning sub-model; Through the flexibility aggregator service application optimization sub-model, the distribution network node load baseline and user energy baseline obtained by the day-ahead energy management baseline scheduling optimization model are used as the benchmark to optimize the flexibility service transaction plan that takes into account the differentiated interests of both the flexibility aggregator and the upper-level distribution network operator; Planning the distribution network operator's purchase of flexibility services from the flexibility aggregator through the distribution network operator's flexibility service procurement planning sub-model; The microgrid optimization dispatch result parsing module is used to solve the distribution network-level flexibility market clearing model using the ADMM algorithm to obtain a distribution-microgrid collaborative optimization dispatch result integrating differentiated building groups.

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