A coordinated operation strategy of a distributed resource park considering a price guiding mechanism

Abstract

The application discloses a kind of park coordination operation strategy containing distributed resource considering price guide mechanism, it is related to park energy management and power distribution network optimization operation field, first, the multiple-agent theory is used, and the multiple-agent model of the operation characteristics of multiple types of distributed resources such as distributed energy, distributed energy storage, controllable load in park is constructed.Further based on the multiple-agent model of the distributed resource constructed, the park coordination operation strategy considering the interactive response characteristics of multiple types of distributed resources is proposed.Interactive interest priority strategy is proposed, the interactive interest of each distributed resource agent is respectively proposed, in each operation time period, park agent responds to each element agent in turn according to the interactive interest priority order of each distributed resource agent.Finally, the real-time interaction relationship between power distribution network operator and park agent is studied, and the power distribution network operator guides the optimal operation of each park coordination operation strategy through the price mechanism, coordinates the optimal operation of multiple independent parks in power distribution network, and further effectively supports the orderly operation of high flexibility power distribution network.

Description

Technical Field

[0001] This invention relates to the field of energy management and power distribution network optimization in industrial parks, specifically a coordinated operation strategy for industrial parks with distributed resources that takes into account price guidance mechanisms. Background Technology

[0002] In recent years, the emergence of various types of integrated industrial parks in distribution networks has accelerated the transformation of distribution networks from centralized to decentralized networks. These parks incorporate energy storage (ES), flexible loads (FL), distributed generation (DG), and smart meters, enabling proactive management of their flexible resources for demand-side response (DR). The demand-side response capabilities of these diverse distributed resources within the parks allow integrated industrial parks to flexibly achieve supply and demand balance and alleviate local network operational problems, such as load spikes. Currently, various types of integrated industrial parks have emerged in China, including zero-carbon campuses, commercial buildings, and industrial parks. In the United States, California's Self-Generation Incentive Program (SGIP) provides incentives to support the installation of user-side distributed energy, having funded over 8,890 projects with a distributed energy capacity exceeding 400 MW. Furthermore, New York State has implemented the Reform Energy Vision (REV) strategy to accelerate the penetration of microgrids and the installation of distributed energy at end-users.

[0003] In traditional power distribution networks, distribution network companies possess a natural monopoly over various dispatchable resources. Optimized operation of the distribution network is often achieved through unified regulation and scheduling of system network structure, grid-connected reactive power compensation devices, and distributed energy resources. This is partly because the existing capital in the distribution network is owned by the distribution network companies themselves. While maintaining safe and reliable power supply, these companies have the ability to achieve economic operation by utilizing system resources to obtain higher profits. Another reason is that previous policies did not allow the involvement of third-party capital in the distribution network. Under the premise of adhering to the electricity price standards set by government price departments and the power supply standards set by energy departments, distribution network companies only needed to allocate and optimize system resources with their own profit as the sole objective. However, this situation has been quietly changing in recent years. With the opening up of the distribution network to social capital, incremental capital has begun to flow in continuously. Most existing integrated industrial parks are now independent third-party entities, each autonomously scheduling resources with the goal of maximizing its own economic benefits. The functions of distribution network companies are being redefined. They can no longer directly control distributed resources within industrial parks for demand-side response. Therefore, price incentive mechanisms should be considered to encourage parks to actively participate in the optimization of distribution network operations. In this new context, distribution network operators must fulfill their responsibilities. While ensuring the safe supply of electricity within the system and guaranteeing the provision of surplus / deficient power, they also need to consider the interests of different market players. Therefore, distribution network operators urgently need to design price guidance mechanisms for various types of integrated industrial parks, using market mechanisms to guide the optimal allocation of different resources, thereby achieving coordinated and optimized operation of the distribution network. Summary of the Invention

[0004] In view of the above-mentioned technical shortcomings, the present invention provides a coordinated operation strategy for distributed resource parks that takes into account price guidance mechanisms.

[0005] To achieve the above-mentioned objectives, the technical solution of the present invention is as follows:

[0006] A coordinated operation strategy for distributed resource parks that takes into account price guidance mechanisms includes the following steps:

[0007] Step 1: Construct an optimal scheduling model for the industrial park that includes various distributed resources. Use multi-agent theory to model the distributed resources and obtain multiple distributed resource agents. At the same time, based on the second-order cone relaxation theory, establish an optimal scheduling model for the distribution network operator and obtain the corresponding guiding marginal electricity price for each industrial park.

[0008] Step 2: Based on the principle of prioritizing interactive benefits, determine the interactive benefits of each distributed resource agent and prioritize these interactive benefits.

[0009] Step 3: Each park coordinates and optimizes its response according to the guiding marginal electricity price and in order of priority.

[0010] Preferably, in step 1, the park optimization scheduling model is:

[0011]

[0012]

[0013] In the formula: This represents the response power of the d-th distributed resource agent in the n-th park at time t; This represents the response electricity price at time t for the nth park; This represents the power of the guaranteed transaction between the nth park and the distribution network operator at time t; This represents the price of the guaranteed transaction between the nth park and the power distribution network operator at time t.

[0014] Preferably, the park optimization scheduling model further includes real-time park operation constraints, which are:

[0015]

[0016]

[0017]

[0018]

[0019] In the formula: This represents the output power of the d-th distributed power agent in the n-th park at time t; This represents the upper limit of the output power of the d-th distributed power agent in the n-th park at time t; This represents the load power of the nth park's dth controllable load agent at time t; This represents the upper and lower limits of the load power at time t for the nth park and the dth controllable load agent; This represents the charging and discharging power of the nth park and the dth energy storage agent at time t; This represents the upper and lower limits of the charging and discharging power of the nth park and the dth energy storage agent at time t; This represents the initial energy of the d-th energy storage agent in the n-th park; This represents the charging and discharging power efficiency of the d-th energy storage agent in the n-th park; This represents the upper limit of the energy storage capacity of the d-th energy storage agent in the n-th park.

[0020] Preferably, in step 1, the distribution network optimization scheduling model can be modeled as follows:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] (V i,t ) 2 -(V j,t ) 2 =2(r ij P ij,t +x ij Q ij,t )-(I ij,t ) 2 [(r ij ) 2 +(x ij ) 2 ]

[0027] (V i,t ) 2 (I ij,t ) 2 =(P ij,t ) 2 +(Q ij,t ) 2

[0028]

[0029]

[0030]

[0031]

[0032] In the formula: π(i) is the set of the first and last nodes of the branch with node i as the last node, φ(i) is the set of the last nodes of the branch with node i as the first node, and r ki x ki The resistance and reactance of branch ki are respectively, r ij x ij The resistance and reactance of branch ij are respectively. ki,t and I ij,t P represents the current in branches ki and ij, respectively. ki,t Q ki,t P represents the active and reactive power of branch ki, respectively. ij,t Q ij,t These represent the active and reactive power of branch ij, respectively. and These represent the power purchased from the distribution network by the industrial park, n=i, respectively. and These represent the active and reactive power of the original load, respectively. V i,t and V j,t Indicates node voltage; S ij V represents the upper limit of the apparent power of branch ij; min / V max Indicates the upper and lower limits of the node voltage; I ij,max This indicates the upper limit of the node current.

[0033] Preferably, in step 1, the distributed resources include distributed energy, adjustable load, energy storage, and conventional load.

[0034] Preferably, in step 1, the distributed resource agent consists of multiple small-capacity distributed resources of the same type within the park, which are aggregated and uniformly controlled by the distributed resource agent.

[0035] Preferably, in step 2, when the interactive benefits are sorted and multiple parks respond in order of priority, the park with the highest confirmed interactive benefit value is the first response.

[0036] Preferably, in step 3, the guiding marginal electricity price for each park is the marginal electricity price of the distribution network node where the park is located.

[0037] The beneficial effects of this invention are:

[0038] This invention first employs multi-agent theory to construct a multi-agent model of the operational characteristics of various distributed resources within a distribution park, including distributed energy, distributed energy storage, and controllable loads. Based on this model, a coordinated operation strategy for the distribution park considering the interactive response characteristics of multiple distributed resources is proposed. An interaction benefit priority strategy is proposed, outlining the interaction benefits for each distributed resource agent. During each operating period, the park agents respond to each component agent sequentially according to the priority of their interaction benefits. Finally, the real-time interaction between the distribution network operator and the park agents is studied. The distribution network operator uses a pricing mechanism to guide the optimization of the coordinated operation strategy for each park, coordinating the optimized operation of multiple independent parks within the distribution network, thereby effectively supporting the orderly operation of a highly resilient distribution network. Attached Figure Description

[0039] Figure 1 A flowchart of a multi-market participation strategy for wind farm energy storage power stations that takes into account the dual uncertainties of power output forecasting and price.

[0040] Figure 2A schematic diagram of the park-based optimal scheduling model in a multi-market participation strategy for wind farm energy storage power stations, taking into account the dual uncertainties of output forecasting and price.

[0041] Figure 3 A flowchart illustrating the demand-side response within a wind farm energy storage power station's multi-market participation strategy, taking into account both output forecasting and price uncertainties. Detailed Implementation

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0043] like Figure 1 As shown, a coordinated operation strategy for a distributed resource park that takes into account a price guidance mechanism includes the following steps:

[0044] Step 1: Construct an optimal scheduling model for the industrial park that includes various distributed resources. Use multi-agent theory to model the distributed resources and obtain multiple distributed resource agents. At the same time, based on the second-order cone relaxation theory, establish an optimal scheduling model for the distribution network operator and obtain the corresponding guiding marginal electricity price for each industrial park.

[0045] Step 2: Based on the principle of prioritizing interactive benefits, determine the interactive benefits of each distributed resource agent and prioritize these interactive benefits.

[0046] Step 3: Each park coordinates and optimizes its response according to the guiding marginal electricity price and in order of priority.

[0047] like Figure 2 As shown, further, in step 1, the park optimization scheduling model is as follows:

[0048]

[0049] In the formula: This represents the response power of the d-th distributed resource agent in the n-th park at time t; This represents the response electricity price at time t for the nth park; This represents the power of the guaranteed transaction between the nth park and the distribution network operator at time t; This represents the price of the guaranteed transaction between the nth park and the power distribution network operator at time t.

[0050] Furthermore, the park optimization scheduling model also includes real-time park operation constraints, which are:

[0051]

[0052]

[0053]

[0054]

[0055] In the formula: This represents the output power of the d-th distributed power agent in the n-th park at time t; This represents the upper limit of the output power of the d-th distributed power agent in the n-th park at time t; This represents the load power of the nth park's dth controllable load agent at time t; This represents the upper and lower limits of the load power at time t for the nth park and the dth controllable load agent; This represents the charging and discharging power of the nth park and the dth energy storage agent at time t; This represents the upper and lower limits of the charging and discharging power of the nth park and the dth energy storage agent at time t; This represents the initial energy of the d-th energy storage agent in the n-th park; This represents the charging and discharging power efficiency of the d-th energy storage agent in the n-th park; This represents the upper limit of the energy storage capacity of the d-th energy storage agent in the n-th park.

[0056] Each park contains different types of distributed resources, including distributed energy, adjustable loads, energy storage, and conventional loads. The park can adjust the optimal operating points of these distributed resources within its control area. Using multi-agent theory, the distributed resources within the park are modeled as multiple distributed resource agents. A distributed resource agent (DERA) consists of multiple small-capacity distributed resources of the same type within the park. Through the aggregation and unified control of these distributed resource agents, small-capacity distributed resources can participate in the demand response process within the park. The park coordinates the demand-side response benefits of the distributed resource agents to maximize the economic surplus within the park.

[0057] like Figure 3As shown, based on the principle of prioritizing interactive benefits, the park responds to multiple distributed resource agents within the park in order of benefit priority. In each iteration, the park selects an appropriate DERA response power according to the interactive benefit ranking until a balance is reached or the distributed resource response energy is no longer available. Specifically, the interactive benefits of distributed power agent, energy storage agent, and controllable load agent are shown in formulas (6)-(8).

[0058]

[0059]

[0060]

[0061] In the formula: This represents the net power of the nth park at time t. This indicates that the nth park has excess power at time t. This indicates that the power of the nth park is insufficient at time t; This represents the guiding electricity price for the distribution network at time t in the nth industrial park; This represents the on-grid electricity price at time t. This represents the time-of-use electricity price of the distribution network at time t.

[0062] Whenever an imbalance in power occurs within the distribution network, the network broadcasts the imbalance power and the guidance price (the marginal price at each node provided by the distribution network operator) to each distributed resource agent. Each agent then considers its own technical constraints and corresponding interaction benefits to generate a feasible interaction response strategy. Each agent then sends its actual response energy and interaction benefits back to the network. Finally, the network summarizes the interaction benefits of each agent and prioritizes them.

[0063] The park allocates response quotas to each distributed resource agent based on the interaction benefits in each bidding segment. The eligibility of each bidding segment allows the successful bidder to respond to P. Δ The power supply quantity is then adjusted, and the park and distributed resource agents repeat the above process until power balance is achieved within the park, or all available distributed resource power has been fully responded to. The specific response process is as follows: Figure 2 As shown.

[0064] Furthermore, in step 1, the distribution network optimization scheduling model can be modeled as follows:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] (V i,t ) 2 -(V j,t ) 2 =2(r ij P ij,t +x ij Q ij,t )-(I ij,t ) 2 [(r ij ) 2 +(x ij ) 2 (14)

[0071] (V i,t ) 2 (I ij,t ) 2 =(P ij,t ) 2 +(Q ij,t ) 2 (15)

[0072]

[0073]

[0074]

[0075]

[0076] In the formula: π(i) is the set of the first and last nodes of the branch with node i as the last node, φ(i) is the set of the last nodes of the branch with node i as the first node, and r ki x ki The resistance and reactance of branch ki are respectively, r ij x ij The resistance and reactance of branch ij are respectively. ki,t and I ij,t P represents the current in branches ki and ij, respectively. ki,t Q ki,t P represents the active and reactive power of branch ki, respectively. ij,t Q ij,t These represent the active and reactive power of branch ij, respectively. and These represent the power purchased from the distribution network by the industrial park, n=i, respectively. and These represent the active and reactive power of the original load, respectively. Vi,t and V j,t Indicates node voltage; S ij V represents the upper limit of the apparent power of branch ij; min / V max Indicates the upper and lower limits of the node voltage; I ij,max This indicates the upper limit of the node current.

[0077] Based on the distribution network optimization operation model given by formulas (9)-(19), the calculation method of the marginal electricity price of the distribution network node can be obtained as shown in formula (20).

[0078]

[0079] The functions A1, ..., A5 are obtained based on AC optimal power flow calculations and are P l,t Q l ,t,I l,t The nonlinear function is shown in formulas (21)-(25). As shown in formula (20), the calculated marginal electricity price of the distribution network node quantifies the influence of the constraints in formulas (6)-(16). The marginal price of the node can represent the influence of distribution line loss, power flow restriction and node voltage restriction.

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] Furthermore, in step 1, the distributed resources include distributed energy, adjustable loads, energy storage, and conventional loads.

[0086] Furthermore, in step 1, the distributed resource agent consists of multiple small-capacity distributed resources of the same type within the park, which are aggregated and uniformly controlled through the distributed resource agent.

[0087] Furthermore, in step 2, when the interactive benefits are sorted and multiple parks respond in order of priority, the park with the highest confirmed interactive benefit value is the first responder.

[0088] Furthermore, in step 3, the guiding marginal electricity price for each park is the marginal electricity price of the distribution network node where the park is located.

[0089] Based on the marginal electricity price of the distribution network nodes calculated by the distribution network operator, the guiding electricity price of the distribution network issued to each park is the marginal electricity price of the distribution network node where the park is located, which can be expressed as:

[0090]

[0091] Based on the park guidance price given by formula (26), each park independently implements the demand-side response strategy given in Part 2, determines the response power and net power under the current guidance price at the current moment, and participates in the distribution network coordination and optimization operation process.

Claims

1. A distributed resource park coordinated operation strategy considering price guiding mechanism, characterized in that, The method comprises the following steps: Step 1: constructing a park optimization scheduling model comprising a plurality of distributed resources, modeling the distributed resources by using a multi-agent theory to obtain a plurality of distributed resource agents; and establishing a power distribution network operator optimization scheduling model based on a second-order cone relaxation theory to obtain a corresponding guiding marginal electricity price of each park; Step 2: determining the interactive interests of each distributed resource agent based on an interactive interest priority principle, and performing priority sorting on the interactive interests; Step 3: each park performs coordinated optimization response according to the priority order according to the guiding marginal electricity price. In the step 1, the park optimization scheduling model is: ； In the formula: represents the response power of the nth park dth distributed resource agent at time t; represents the response price of the nth park at time t; represents the power of the nth park at time t and the guaranteed transaction with the distribution network operator; represents the price of the nth park at time t and the guaranteed transaction with the distribution network operator.

2. The strategy for coordinating operation of a distributed resource park with a price-guided mechanism according to claim 1, wherein, The park optimization scheduling model further comprises a park real-time operation constraint, and the park real-time operation constraint is: ； ； ； ； In the formula: This represents the output power of the d-th distributed power agent in the n-th park at time t; This represents the upper limit of the output power of the d-th distributed power agent in the n-th park at time t; This represents the load power of the nth park's dth controllable load agent at time t; and This represents the upper and lower limits of the load power at time t for the nth park and the dth controllable load agent; and This represents the charging and discharging power of the nth park and the dth energy storage agent at time t; and This represents the upper and lower limits of the charging and discharging power of the nth park and the dth energy storage agent at time t; Indicates the first n The first park d The initial energy of the energy storage agent; and Indicates the first n The first park d The charging and discharging power efficiency of an energy storage agent; Indicates the first n The first park d The maximum storage capacity of an energy storage agent.

3. The strategy for coordinating operation of a distributed resource park with a price-guided mechanism according to claim 1, wherein, In the step 1, the power distribution network operator optimization scheduling model is modeled as: ； ； ； ； ； ； ； ； ； ； In the formula: π i For nodes i Let be the set of the starting nodes of the branches whose ending nodes are . For nodes i The set of branch end nodes with the first node. r ki , x ki Branch roads k i Resistance and reactance, r ij , x ij Branch roads ij Resistance and reactance, I ki,t and I ij,t Branch roads k i 、 branch road ij The current, P ki,t , Q ki,t Branch roads k i Active and reactive power, P ij,t , Q ij,t Branch roads ij Active and reactive power, and They represent the park n=i Power purchased from the distribution network and These represent the active and reactive power of the original load, respectively. V i,t and V j,t Indicates node voltage; S ij Indicates a branch ij The apparent power limit; V max and V min Vmin and Vmax represent the lower and upper node voltage limits, respectively; I ij,max represents the upper limit of the node current.

4. The strategy for coordinating operation of a distributed resource park with a price-guided mechanism according to claim 1, wherein, In the step 1, the distributed resources comprise distributed energy, adjustable load, energy storage and conventional load.

5. The strategy for coordinating operation of a park with distributed resources considering price leading mechanism according to claim 1, characterized in that, In the step 1, the distributed resource agent is composed of a plurality of small-capacity distributed resources of the same type in the park, and the aggregation and unified control of the distributed resource agent are performed.

6. The strategy for coordinating operation of a distributed resource park with a price- leading mechanism according to claim 1, wherein, In the step 2, when the plurality of parks respond in turn according to the priority order, the highest value of the interactive income confirmed by the park is taken as the first response.

7. The strategy for coordinating operation of a park with distributed resources considering price leading mechanism according to claim 1, characterized in that, In the step 3, the guiding marginal electricity price of each park is the power distribution network node marginal electricity price of the node where the park is located.

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