A multi-energy complementary three-layer optimization operation method suitable for a combined heat and power microgrid
By constructing a multi-energy complementary three-layer optimized operation method suitable for cogeneration microgrids, the problems of energy transmission loss and operating costs in cogeneration microgrids are solved, achieving lower energy loss and lower operating costs.
Patent Information
- Application Number
- CN202211143962.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-09-20
- Publication Date
- 2025-12-05
- Estimated Expiration
- 2042-09-20
AI Technical Summary
Existing technologies fail to effectively balance the power comfort and operating costs of multiple microgrids in a combined heat and power microgrid, and there are also issues with energy loss during the transmission process.
A three-layer optimization operation method for multi-energy complementarity is constructed, including a lower-layer optimization operation model that aims to minimize the operating cost of microgrids, an intermediate-layer optimization model that optimizes the energy trading path between microgrids through the Floyd-Warshall algorithm, and an upper-layer model that selects appropriate trading routes to reduce the total cost.
It improves the energy sharing capability between microgrids, reduces energy loss during energy transmission, and reduces the operating cost of microgrids.
Smart Images
Figure CN115511168B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of cogeneration microgrid optimization, and particularly relates to a multi-energy complementary three-layer optimization operation method suitable for a cogeneration microgrid. BACKGROUND
[0002] With the increasing depletion of traditional energy, how to fully develop and utilize renewable energy and improve energy utilization rate has become an urgent problem. The cogeneration microgrid integrates heat supply and power supply, realizes energy cascade utilization through internal cogeneration units, improves energy utilization rate and system operation flexibility. However, the energy structure and equipment coupling relationship in the cogeneration microgrid are complex, which increases the difficulty of system operation and management, and the intermittent nature of renewable energy affects the safe and reliable operation of the system. Therefore, an optimization operation method suitable for the cogeneration microgrid is needed to realize the coordinated and optimized operation of renewable energy, energy supply equipment and energy storage equipment in the system.
[0003] In the prior art document: document [1]: Community Microgrid Energy Storage Capacity Configuration Considering Electric Vehicles, Lu Yanjuan, Pan Tinglong, Yang Zhaohui. Community Microgrid Energy Storage Capacity Configuration Considering Electric Vehicles [J]. Solar Energy, 2021, 42(12): 363-367. A CHP microgrid comprehensive operation cost optimization scheduling model is established to minimize the CHP microgrid comprehensive operation cost. This model solves the problems of high operation cost of traditional battery energy storage and poor microgrid operation stability.
[0004] Document [2]: Economic Optimization Dispatching Strategy of Microgrid for Promoting Photoelectric Consumption Considering Cogeneration and Demand Response (DOU C, ZHOU X, ZHANG T, et al. Economic Optimization Dispatching Strategy of Microgrid for Promoting Photoelectric Consumption Considering Cogeneration and Demand Response [J]. Journal of modern power systems and clean energy, 2020, 8(3): 557-563.) establishes a load demand response model for the time-of-use pricing strategy of distribution network. This model greatly reduces the operation cost of the system.
[0005] Document [3]: "Cooperative Optimization Dispatching of Multiple Microgrids Based on Cooperative Game Considering Conditional Value at Risk" (Xuan Yue, Xiu-Li Wang, Xiong Wu, et al. Cooperative Optimization Dispatching of Multiple Microgrids Based on Cooperative Game Considering Conditional Value at Risk [J]. Power System Technology, 2022, 46(1): 130-137.) proposes a cooperative optimization strategy based on cooperative game, which realizes the optimal operation of CHP type microgrid.
[0006] However, the optimal operation methods in the above documents do not take into account the power comfort and operation cost of multiple microgrids, and there is a problem of not considering energy loss in the transmission process. SUMMARY
[0007] To solve the above technical problems, the present application provides a multi-energy complementary three-layer optimal operation method suitable for combined heat and power (CHP) type microgrid, which constructs a lower layer optimal operation model with the minimum operation cost of each microgrid as the target, an upper layer optimal operation model with the minimum interaction cost between each subject as the target, and an intermediate layer optimal operation model with the minimum energy transmission path loss as the target. The method first optimizes the microgrid with the minimum operation cost as the target, then improves the energy mutual aid ability between microgrids through barter trade and buy-sell trade in the intermediate layer model, and searches for the optimal transmission path for energy trade between microgrids based on the Floyd-Warshall algorithm, and finally selects the appropriate route for trade with the outside by the upper layer. The method has good applicability, and compared with other optimization strategies, the energy transmission process loss is lower, the energy mutual aid ability between microgrids is stronger, and the operation cost of each microgrid is smaller.
[0008] The technical scheme adopted by the present application is:
[0009] A multi-energy complementary three-layer optimal operation method suitable for combined heat and power type microgrid, comprising the following steps:
[0010] Step 1: Construct a lower layer optimal operation model with the minimum operation cost of each microgrid as the target, and each combined heat and power type microgrid optimizes individually with the minimum operation cost as the target, and transmits the internal electric information, thermal information, remaining amount, and shortage amount information to the intermediate layer optimal operation model for the next energy optimization;
[0011] Step 2: The intermediate layer optimal operation model takes the minimum heat and electric energy transmission path loss as the target, optimizes the barter trade and buy-sell trade of energy between microgrids using the Floyd-Warshall algorithm, and transmits the remaining amount, shortage amount, and increaseable amount information of heat and electric energy of each combined heat and power type microgrid to the upper layer optimal operation model.
[0012] Step 3: The upper layer optimization model solves according to the total surplus or shortage of electric energy and thermal energy, and selects the appropriate route to trade with the outside.
[0013] In step 1, the lower layer optimization model takes the minimum operation cost as the optimization objective, and the specific expression is as follows:
[0014]
[0015] In the formula: C fuel is the fuel cost of CHP; C om is the internal operation and maintenance cost of the combined heat and power microgrid; is the start-stop cost of CHP.
[0016] The fuel cost of CHP is as follows:
[0017]
[0018] In the formula: R ng is the unit price of fuel; H ng is the unit heat value of fuel; P t CHP,E is the electric power of CHP unit; η CHP is the power generation efficiency of CHP unit; t is the time period of day-ahead scheduling.
[0019] The operation and maintenance cost of the combined heat and power microgrid is as follows:
[0020]
[0021] In the formula: is the maintenance cost of CHP; is the maintenance cost of photovoltaic; is the maintenance cost of energy storage;
[0022] k CHP , k PV are the operation and maintenance cost coefficients of CHP and photovoltaic, respectively; is the maintenance cost coefficient of electric energy storage; is the maintenance cost coefficient of thermal energy storage;
[0023] are the charging and discharging efficiencies of electric energy storage, respectively; are the charging and discharging efficiencies of thermal energy storage, respectively.
[0024] P t PV is the output power of photovoltaic; is the charging power of electric energy storage; is the discharging power of electric energy storage.
[0025] The start-up and shutdown costs of CHP are shown below:
[0026]
[0027] In the formula: Let CHP be the state variable during time period t; These are the start-up and shutdown cost coefficients for CHP, respectively. Let be the state variable of CHP during the time interval t-1.
[0028] Constraints:
[0029] The constraints mainly include CHP output power constraints, electrical and thermal power balance constraints, and energy storage constraints.
[0030] The CHP output power constraint is:
[0031]
[0032]
[0033] In the formula: These represent the minimum and maximum output power of CHP, respectively. The thermoelectric output ratio of CHP; P represents the thermal power output of CHP. t CHP,E This refers to the electrical power output by CHP.
[0034] The power balance constraints for electrical and thermal loads are:
[0035]
[0036]
[0037] In the formula: P t E,sur P t E,short These refer to the excess and shortage of power in combined heat and power microgrids. These refer to the excess and shortage of thermal energy in combined heat and power microgrids. These are the electrical and thermal loads for combined heat and power (CHP) systems. t BS,E P represents the electrical power of energy storage. t BS,H P is the thermal power of energy storage; t CHP,E This refers to the electrical power of the CHP unit.
[0038] The energy storage constraints for combined heat and power microgrids are:
[0039]
[0040]
[0041]
[0042]
[0043]
[0044]
[0045] In the formula: These are the maximum charging and discharging power of the combined heat and power microgrid energy storage, respectively.
[0046] These are the maximum charging and discharging power of the combined heat and power microgrid thermal energy storage, respectively.
[0047] These represent the minimum and maximum energy storage capacities of a combined heat and power (CHP) microgrid.
[0048] These represent the minimum and maximum thermal energy storage capacities of a combined heat and power (CHP) microgrid.
[0049] These represent the electrical and thermal energy storage capacities of a combined heat and power microgrid during time period t.
[0050] In step 2, each cogeneration microgrid provides its own electricity and heat purchase / sale price based on its own energy situation:
[0051]
[0052]
[0053] In the formula: f 1,t For the operating cost of a combined heat and power microgrid; P t PV +P t CHP,E -P t BS,E Net power generation of a combined heat and power microgrid; P t CHP,H -P t BS,H This refers to the net heat generation of a combined heat and power (CHP) microgrid; when the CHP microgrid is in a state of purchasing electricity and heat. and These are the prices for purchasing electricity and heat, respectively; when a combined heat and power (CHP) microgrid is in a state of selling electricity and heat... and These are the prices for electricity and heat sales, respectively.
[0054] In step 2, the line loss is calculated using a DC approximation method:
[0055] P loss,i =r i P i 2 / V i 2 ;
[0056] In the formula: P loss,i r represents the line loss power of the i-th transmission line between cogeneration microgrids; i P is the line resistance of the i-th transmission line; i V represents the active power transmitted by the i-th transmission line; i This refers to the voltage level of the transmission line.
[0057] The heat loss during the transmission process of heating pipelines is calculated using the nodal method:
[0058] The temperature at the end of the heating pipeline during transmission is:
[0059]
[0060] Where: C is the heat loss coefficient during the transmission process of the heating pipeline in a combined heat and power microgrid; τ i T is the thermal delay time during the transmission of heat through the pipeline; i in λ is the initial temperature of the heating pipeline during transmission; λ is the loss coefficient of the heating pipeline during transmission; c i Specific heat capacity of the heat transfer medium in the heating pipeline of a combined heat and power microgrid; T m For ambient temperature; l i Let m be the length of the i-th combined heat and power microgrid heating pipeline; i The weight of the heat transfer medium in the heating pipeline of a combined heat and power microgrid.
[0061] The delay time during the transmission process of heating pipelines is:
[0062]
[0063] In the formula: Δτ is the transmission time error of the heating pipeline in a combined heat and power microgrid; l i d i These represent the length and radius of the heating pipe, respectively; ρ w m i These are the density and mass of the heat transfer medium, respectively.
[0064] Based on power line losses and the characteristics of the heat network, the electrothermal revenue function of a combined heat and power (CHP) microgrid is as follows:
[0065]
[0066]
[0067]
[0068]
[0069]
[0070]
[0071] In the formula: For the electricity purchase revenue function of a combined heat and power microgrid; For the electricity sales revenue function of a combined heat and power microgrid; For the heat purchase revenue function of a combined heat and power microgrid; For the heat sales revenue function of a combined heat and power microgrid;
[0072] These are respectively the combined heat and power microgrid electricity and heat purchase volume; These are the electrical and heat losses of a combined heat and power microgrid; These are respectively the combined heat and power microgrid electricity and heat sales volume. These are the purchase and sale prices of electricity for the two combined heat and power microgrids, respectively. These are the purchase and sale prices of heat for the two combined heat and power microgrids, respectively.
[0073] The electricity trading prices for combined heat and power (CHP) microgrids are as follows:
[0074]
[0075] For combined heat and power (CHP) microgrid heat trading prices, there are:
[0076]
[0077] In step 2, the objective function of the optimized operation model of the intermediate layer of the cogeneration microgrid is:
[0078]
[0079] In the formula: U and M are the total number of electricity and heat transactions of cogeneration microgrids, respectively; u is the number of cogeneration microgrids for electricity transactions; and m is the number of cogeneration microgrids for heat transactions.
[0080] For the electricity purchase revenue function of a combined heat and power microgrid; For the electricity sales revenue function of a combined heat and power microgrid; For the heat purchase revenue function of a combined heat and power microgrid; For the heat sales revenue function of a combined heat and power microgrid;
[0081] Constraints of combined heat and power microgrids:
[0082] 1) Purchase / sale price constraints:
[0083]
[0084]
[0085] 2) Purchase / sales volume constraints:
[0086]
[0087]
[0088] In the formula: These represent the maximum power purchase and sales values for a combined heat and power (CHP) microgrid. Δt represents the maximum heat purchase and sales values for a combined heat and power microgrid; Δt is the scheduling step size.
[0089] In step 2, the intermediate layer optimization operation model improves the energy mutual assistance capability between cogeneration microgrids through barter transactions. When microgrid n has surplus electricity but lacks heat, while microgrid m has surplus heat but lacks electricity, microgrid n and microgrid m are paired through barter transactions. The surplus electricity of microgrid n is transferred to microgrid m, and the surplus heat of microgrid m is transferred to microgrid n, thereby achieving energy complementarity between microgrids.
[0090] In step 2, the Floyd-Warshall algorithm is used to select the optimal path for energy trading between cogeneration microgrids, including the following steps:
[0091] Step 2.1: Let N(V,A) be a cogeneration microgrid connection network, where V={1,2,3,…,n} is the set of nodes of the cogeneration microgrid, and |V|=n; A={(i,k):i,k∈V,i≠k} is the edge set between two cogeneration microgrids. i represents the i-th cogeneration microgrid; k represents the k-th cogeneration microgrid.
[0092] Step 2.2: Set D j R j (j=0,1,…,n) is an n×n matrix in the combined heat and power microgrid connection network.
[0093] j is the order, n is the total number of network nodes, where D j Let R be the path matrix. j This is the precursor matrix.
[0094] Step 2.3: When j = 0:
[0095] At this time, D0 = [d ik ]:
[0096]
[0097] D0 represents the 0th-order path matrix; d ik This represents the path distance between micronet i and micronet k.
[0098] At this time, R0 = [r ik ]:
[0099]
[0100] R0 represents the 0th-order predecessor matrix; r ik This represents the midpoint on the shortest path from micronet i to micronet k.
[0101] Step 2.4: When j = 1:
[0102] At this time, D1 = [d ik ]
[0103]
[0104] D1 represents the first-order path matrix; d ij d represents the path distance between micronet i and micronet j. jk This represents the path distance between micronet j and micronet k.
[0105] At this time, R1 = [r ik ]
[0106]
[0107] R1 represents the first-order predecessor matrix; r ik This represents the midpoint on the shortest path from micronet i to micronet k.
[0108] Step 2.5: Repeat step 2.5 until j = n, at which point the optimal transmission path between any two cogeneration microgrids can be obtained.
[0109] In step 3, the objective function of the upper-level optimization running model is:
[0110]
[0111] In the formula: The electrical interaction cost between the combined heat and power microgrid and the distribution network; The cost of thermal interaction between the combined heat and power microgrid and the external heating network system; For electricity loss costs, Cost of heat loss.
[0112] The total electrothermal interaction cost of a combined heat and power microgrid is shown in the following formulas:
[0113]
[0114]
[0115] In the formula: These are respectively the combined heat and power microgrid power and heat interaction power; For the power distribution network's purchase / sale price; x pur,t x sel,t These are the status variables for combined heat and power (CHP) micro-grid purchases and electricity sales; These are the purchase and sale prices of heat for the heating network system; y pur,t y sel,t These are the status variables for combined heat and power (CHP) micro-network purchase and heat sales.
[0116] The costs of electrical and thermal losses in a combined heat and power (CHP) microgrid are shown in the following formulas:
[0117]
[0118]
[0119] In the formula: r is the radius of the combined heat and power microgrid heating pipeline; T in T out , where represents the temperature at the beginning and end of the cogeneration microgrid heating pipeline; c represents the specific heat capacity of the medium transported within the cogeneration microgrid heating pipeline; and m represents the mass of the medium transported within the cogeneration microgrid heating pipeline.
[0120] Constraints of combined heat and power microgrids:
[0121]
[0122]
[0123] In the formula: These are the minimum and maximum values of the interaction power between the combined heat and power microgrid and the distribution network, respectively. The minimum and maximum values of the interactive heat power between the combined heat and power microgrid and the heating network system; The electrical power exchanged between the combined heat and power microgrid and the heating network system; This refers to the heat power exchanged between the combined heat and power microgrid and the heating network system.
[0124] This invention discloses a multi-energy complementary three-layer optimized operation method applicable to CHP-type cogeneration microgrids, with the following technical advantages:
[0125] 1) The energy trading method between microgrids proposed in this invention combines barter and buying and selling transactions. This trading method can gradually improve the energy mutual assistance capability between microgrids and reduce the dependence of microgrids on external distribution networks and heating networks.
[0126] 2) This invention uses the Floyd-Warshall algorithm to optimize the barter and buying / selling of energy between microgrids. This algorithm can find the shortest distance between two points without traversing all paths in the network graph, which greatly reduces the computation time of the algorithm.
[0127] 3) Unlike other cogeneration microgrid optimization processes, this invention considers losses during energy trading, including line losses during power transmission and thermal delays and losses during heat transmission. Considering these transmission losses allows for better optimization of the cogeneration microgrid's operation. Attached Figure Description
[0128] Figure 1 This is a flowchart of the operation method proposed in this invention.
[0129] Figure 2 This is a schematic diagram of the structure of a combined heat and power microgrid system.
[0130] Figure 3(a) shows the power dispatch results of each microgrid CHP over 24 hours;
[0131] Figure 3(b) shows the results of CHP thermal power scheduling for each microgrid within 24 hours.
[0132] Figure 4(a) shows the charging and discharging diagram of each microgrid's energy storage over 24 hours;
[0133] Figure 4(b) shows a schematic diagram of the thermal energy storage charging and discharging of each microgrid within 24 hours;
[0134] Figure 5 This is a graph showing the results of microgrid group power scheduling for electrical and thermal interactions over a 24-hour period.
[0135] Figure 6 This is a diagram showing the operating costs of each microgrid under the optimization method proposed in this invention. Detailed Implementation
[0136] A three-layer optimization operation method for multi-energy complementarity applicable to combined heat and power (CHP) microgrids is proposed. This method constructs a lower-layer optimization operation model aiming to minimize the operating cost of each microgrid, an upper-layer optimization operation model aiming to minimize the interaction cost between entities, and an intermediate-layer optimization operation model aiming to minimize energy transmission path losses. First, optimization is performed with the goal of minimizing the operating cost of each CHP microgrid. Then, in the intermediate-layer model, energy exchange capabilities between microgrids are improved through barter and sales transactions, and the optimal transmission path for energy trading between microgrids is searched based on the Floyd-Warshall algorithm. Finally, the upper-layer model selects suitable routes for trading with external entities. The method described in this invention has good applicability; compared to other optimization strategies, it exhibits lower energy transmission process losses, stronger energy exchange capabilities between microgrids, and lower operating costs for each microgrid.
[0137] Figure 1 This is a flowchart of a multi-energy complementary three-layer optimized operation method for combined heat and power microgrids proposed in this invention, as shown below. Figure 1 As shown, the method flow is as follows:
[0138] (1) Each cogeneration microgrid is optimized individually with the goal of minimizing operating costs, and the information on the surplus / shortage of electricity / heat within the microgrid is transmitted to the intermediate layer for the next step of energy optimization.
[0139] (2) The intermediate layer aims to minimize the loss of thermoelectric energy transmission path. It uses the Floyd-Warshall algorithm to optimize the barter and trading of energy between microgrids and transmits the information on the surplus / shortage and the amount of energy that can be increased of each cogeneration microgrid to the upper layer.
[0140] (3) The upper layer solves the problem based on the total surplus / shortage of electrical and thermal energy, and selects a suitable route to trade with the outside world. Figure 2 This is a schematic diagram of a combined heat and power (CHP) microgrid system. Figure 2As shown, the system in this example consists of 6 combined heat and power microgrids. Microgrid 1 can exchange electrical energy with microgrids 2, 3, and 6, and can exchange thermal energy with microgrids 2, 3, and 5; Microgrid 2 can exchange electrical energy with microgrids 1, 3, and 5, and can exchange thermal energy with microgrids 1, 4, and 5; Microgrid 3 can exchange electrical energy with microgrids 1, 2, 4, and 6, and can exchange thermal energy with microgrids 1 and 4; Microgrid 4 can exchange electrical energy with microgrids 3 and 6, and can exchange thermal energy with microgrids 2, 3, and 6; Microgrid 5 can exchange electrical energy with microgrids 2 and 6, and can exchange thermal energy with microgrids 1, 2, and 6; Microgrid 6 can exchange electrical energy with microgrids 1, 3, 4, and 5, and can exchange thermal energy with microgrids 4 and 5.
[0141] Figures 3(a), 3(b), 4(a), 4(b) and Figure 5 The figures show the scheduling results of CHP electrothermal power, electrothermal energy storage charging and discharging, and microgrid group electrothermal interaction power for each microgrid over a 24-hour period. (See Figures 3(a), 3(b), 4(a), and 4(b)). Figure 5 As shown, to reduce their own operating costs, each CHP (Concentrated Heating Power) microgrid reduces its own power generation, while the energy storage devices are charging to increase their storage capacity. During this time, the CHP microgrid group increases its electricity purchases, buying electricity at lower prices to supply its own users and the energy storage, thereby reducing the system's fuel costs. Between 11:00 and 14:00 and between 18:00 and 21:00, the distribution network's electricity sales price is at its peak. Each microgrid increases its own power generation through CHP and discharges its energy storage to meet user electricity demand and reduce system electricity costs. The microgrid group reduces its purchases of electricity from the distribution network. Meanwhile, at 21:00, the microgrid system has surplus electricity, which it sells back to the distribution network, further reducing the overall operating costs of the microgrid group.
[0142] Figure 6 This represents the operating cost of each microgrid under the optimized method proposed in this invention. Figure 6 It is evident that, compared to the three-layer optimization strategy proposed in this invention, the total operating cost of each microgrid using the hierarchical autonomous optimization strategy and the collaborative optimization strategy is higher. This is because, during the optimization process, the hierarchical autonomous optimization strategy does not consider energy exchange between microgrids and can only trade energy with external entities at higher prices. Furthermore, the collaborative optimization strategy suffers greater energy and heat loss during energy transmission between microgrids due to the failure to select the optimal transmission path. The three-layer optimization strategy proposed in this invention considers both energy exchange between microgrids and transmission losses, thus resulting in lower operating costs for all six cogeneration microgrids compared to the other two optimization strategies.
[0143] Table 1 Results of Barter Transactions between Cogeneration Microgrids during the 11:00 Period
[0144]
[0145] Table 2 Results of energy trading between cogeneration microgrids at 11:00
[0146]
[0147] Table 3. Electricity and heat interaction costs of cogeneration microgrids, distribution networks, and heating networks under three different optimization strategies.
[0148]
[0149]
[0150] Tables 1 and 2 show the results of barter transactions and energy trading between cogeneration microgrids at 11:00 AM, respectively. As shown in Tables 1 and 2, a multi-energy complementary three-layer optimized operation method suitable for cogeneration microgrids first involves barter transactions between microgrids. Based on the agreed-upon electricity and heat trading prices, each microgrid contributes an equivalent value of electricity and heat energy for trading according to the principle of equivalent exchange. The analysis is based on the barter transactions between cogeneration microgrid 1 and cogeneration microgrid 5. Microgrid 1 has a surplus of electricity but a shortage of heat, while microgrid 5 has a surplus of heat but a shortage of electricity. The electricity and heat trading prices are determined based on their respective bids. Microgrid 1 transmits electricity to microgrid 5, and microgrid 5 transmits heat to microgrid 1, thus achieving energy mutual assistance between the two microgrids.
[0151] After a barter transaction, the remaining / shortage quantities of each microgrid are updated for energy trading. The analysis focuses on the heat energy trading between cogeneration microgrid 3 and cogeneration microgrid 1. At this point, microgrid 3 has surplus heat energy, while microgrid 1 lacks heat energy. Based on their respective heat energy trading quotes, they agree on a trading price. Then, using the Floyd-Warshall algorithm, they select the trading path with the minimum electrical and thermal losses to complete the energy trading between the microgrids, thereby improving the economic efficiency of the microgrids.
[0152] Table 3 shows the electricity and heat exchange costs between the microgrid cluster and the distribution network and heating network system under three different optimization strategies. As shown in Table 3, regarding the electricity transaction costs with the external distribution network and the heat transaction costs with the external heating network system, the optimization strategy proposed in this invention considers factors such as energy mutual assistance in the combined heat and power microgrid and minimizing transmission path losses. Therefore, both transaction costs are lower than those of the hierarchical autonomous optimization strategy and the collaborative optimization strategy. In terms of total transaction costs, the optimization strategy proposed in this invention reduces costs by 23.76% and 10.16% compared to the hierarchical autonomous optimization strategy and the collaborative optimization strategy, respectively.
Claims
1. A multi-energy complementary three-layer optimal operation method suitable for a combined heat and power microgrid, characterized in that The method comprises the following steps: Step 1: constructing a lower-layer optimal operation model with the minimum operation cost of each microgrid as the target, each cogeneration microgrid is individually optimized with the minimum operation cost as the target, and internal electric information, thermal information, residual amount and shortage amount information of the microgrid are transmitted to the middle-layer optimal operation model for next step energy optimization; Step 2: the middle-layer optimal operation model takes the minimum heat and electric energy transmission path loss as the target, adopts the Floyd-Warshall algorithm to optimize barter trade and buy / sell trade among microgrids, and transmits residual amount, shortage amount and increaseable amount information of electric energy and heat energy of each cogeneration microgrid to the upper-layer optimal operation model; Step 3: the upper-layer optimal operation model solves according to total electric energy and heat energy residual or shortage conditions, and selects a suitable route to trade with the outside. In the step 1, the lower-layer optimal operation model takes the minimum operation cost of itself as the optimization target, and the specific expression is as follows: wherein: C fuel is the fuel cost of the CHP; C om is the internal operation and maintenance cost of the microgrid of the cogeneration type; is the CHP start-stop cost; the CHP fuel cost is as follows: wherein: R ng is the fuel unit price; H ng the unit heat value of the fuel; P t CHP,E the electric power of the CHP unit; η CHP the power generation efficiency of the CHP unit; t is the time period of day-ahead scheduling; The internal operation and maintenance cost of the cogeneration microgrid is as follows: wherein: is the maintenance cost for CHP; is the maintenance cost for photovoltaics; is the maintenance cost for energy storage; k CHP , k PV are CHP, photovoltaic operation and maintenance cost coefficients, respectively; is the maintenance cost coefficient of electrical energy storage; is the maintenance cost coefficient of thermal energy storage; charging and discharging efficiency of electrical energy storage, respectively; charging and discharging efficiency of thermal energy storage, respectively; P t PV for the power output of the photovoltaics; for the charging power of the electrical energy storage; for the discharging power of the electrical energy storage; The CHP start-stop cost is as follows: wherein: U t CHP is the state variable of the CHP at time period t; are respectively the start-up and shut-down cost coefficients of the CHP; is the state variable of the CHP at time period t-1; In the step 2, the optimization target function of the middle-layer optimal operation model of the cogeneration microgrid is as follows: In the formula, U and M are respectively the total number of electric and heat trades of the cogeneration microgrid, u is the number of cogeneration microgrids of electric trade, and m is the number of cogeneration microgrids of heat trade; a heat purchase benefit function for the cogeneration microgrid; a heat sale benefit function for the cogeneration microgrid; a heat purchase benefit function for the cogeneration microgrid; a heat sale benefit function for the cogeneration microgrid; The constraint condition of the cogeneration microgrid is as follows: 1) purchase / sale price constraint: 2) purchase / sale amount constraint: In the formulae: respectively, are the maximum values of the purchased and sold electricity of the combined heat and power microgrid; respectively, are the maximum values of the purchased and sold heat of the combined heat and power microgrid; △t is the step length of scheduling; In the step 3, the target function of the upper-layer optimal operation model is as follows: In the formula: is the cost of electrical interaction between the combined heat and power microgrid and the distribution network; is the cost of thermal interaction between the combined heat and power microgrid and the external heat network system; is the cost of electrical loss, is the cost of thermal loss; The total electric and heat interaction cost of the cogeneration microgrid is respectively as follows: In the formula: respectively, are the electric and heat interactive power of the combined heat and power microgrid; is the purchase / sale electricity price of the distribution network;x pur,t , x sel,t respectively, are the purchase / sale electricity state variables of the combined heat and power microgrid; respectively, are the purchase / sale heat price of the heat network system; y pur,t , y sel,t are respectively heat purchase and sale state variables of the microgrid with heat and power cogeneration The heat and electric energy loss cost of the cogeneration microgrid is respectively as follows: In the formula, r is the radius of the heat-supply pipeline of the combined heat and power microgrid; T in , T out are the temperatures at the first and last ends of the heat-supply pipeline of the combined heat and power microgrid, respectively; c is the specific heat capacity of the transmission medium in the heat-supply pipeline of the combined heat and power microgrid; and m is the mass of the transmission medium in the heat-supply pipeline of the combined heat and power microgrid. The constraint condition of the cogeneration microgrid is as follows: In the formula: respectively minimum and maximum of the interactive power between the microgrid of the combined heat and power and the power distribution network; respectively minimum and maximum of the interactive heat power between the microgrid of the combined heat and power and the heat network system; interactive electric power between the microgrid of the combined heat and power and the heat network system; interactive heat power between the microgrid of the combined heat and power and the heat network system.
2. The multi-energy complementary three-layer optimal operation method for a combined heat and power microgrid according to claim 1, wherein: The constraint condition of the lower-layer optimal operation model includes the CHP output power constraint, electric and heat load power balance constraint and energy storage constraint; The CHP output power constraint is as follows: wherein: Pmin, Pmaxare the minimum and maximum electrical power output of the CHP, respectively, Pheat / electricis the heat-to-electricity production ratio of the CHP; P t CHP,H Pheatis the heat power output of the CHP; P t CHP,E Pelecis the electrical power output of the CHP; The electric and heat load power balance constraint is as follows: wherein: P t E,sur P t E,short P t H,sur P t H,short P P t BS,E P t BS,H P t CHP,E P The energy storage constraint of the cogeneration microgrid is as follows: In the formula: respectively, the maximum charging and discharging power of the micro-grid electric energy storage of the combined heat and power type; Qmax,charge and Qmax,discharge are the maximum heat charging and discharging power of the microgrid heat storage, respectively. respectively the minimum and maximum values of the electric energy storage capacity of the microgrid of the cogeneration type; respectively the minimum and maximum values of the thermal storage capacity of the microgrid of the cogeneration type; Electric and thermal energy storage capacity of the microgrid t time period of the combined heat and power type, respectively.
3. The multi-energy complementary three-layer optimal operation method for a combined heat and power microgrid according to claim 1, wherein: In the step 2, each cogeneration microgrid gives electric and heat purchase / sale prices according to its own energy conditions: wherein: f 1,t is the operation cost of the combined heat and power microgrid; P t PV + P t CHP,E - P t BS,E is the net power generation of the combined heat and power microgrid; P t CHP,H - P t BS,H is the net heat generation of the combined heat and power microgrid; when the combined heat and power microgrid microgrid is in the state of purchasing power and heat, and are the purchasing power and heat prices, respectively; when the combined heat and power microgrid microgrid is in the state of selling power and heat, and The prices are respectively electric and heat sale prices.
4. The multi-energy complementary three-layer optimal operation method for a combined heat and power microgrid according to claim 1, wherein: In the step 2, the line loss is calculated in a direct current approximate manner: P loss,i = r i P i 2 / V i 2 ; In the formula, P loss,i is the line loss power of the ith transmission line between the microgrid of heat and power supply type. r i Ri is the line resistance for the i-th transmission line; P i Vp,i is the transmitted active power for the i-th transmission line; V i Vp is the voltage level of the transmission line; The heat loss in the heat supply pipeline transmission process is calculated by using the node method: The end temperature in the heat supply pipeline transmission process is as follows: In the formula, C is the heat loss coefficient in the heat supply pipeline transmission process of the combined heat and power microgrid; τ i is the heat delay time in the heat supply pipeline transmission process. T i in T is the temperature of the first end of the heat supply pipeline in the heat supply pipeline transmission process; λ is the loss coefficient of the heat supply pipeline in the heat supply pipeline transmission process; c i T is the specific heat capacity of the heat transmission medium of the heat supply pipeline of the combined heat and power microgrid; T m T is the ambient temperature; l i l is the length of the i-th heat supply pipeline of the combined heat and power microgrid; m i m is the weight of the heat transmission medium of the heat supply pipeline of the combined heat and power microgrid; The delay time in the heat supply pipeline transmission process is as follows: In the formula: △τ is the heat supply pipeline transmission time error of the combined heat and power microgrid; l i , d i are the length and radius of the heat supply pipeline respectively; ρ w , m i are the density and mass of the heat transmission medium respectively; Based on the electric energy line loss and the heat energy heat network characteristics, the electric and heat income function of the cogeneration microgrid is as follows: In the formula: is a heat and power cogeneration microgrid electricity purchase revenue function; is a heat and power cogeneration microgrid electricity sale revenue function; is a heat and power cogeneration microgrid heat purchase revenue function; is a heat and power cogeneration microgrid heat sale revenue function; respectively, the electricity and heat purchase amounts of the cogeneration microgrid; respectively, the electricity and heat loss of the cogeneration microgrid; respectively, the electricity and heat sale amounts of the cogeneration microgrid; respectively, the electricity purchase and sale prices given by the two cogeneration microgrids; respectively, the heat purchase and sale prices given by the two cogeneration microgrids; The electricity transaction price for the combined heat and power microgrid is: The heat transaction price of the combined heat and power microgrid is:
5. The multi-energy complementary three-layer optimal operation method for a combined heat and power microgrid according to claim 1, wherein: In the step 2, the middle-layer optimal operation model improves the energy mutual aid ability among cogeneration microgrids through barter trade, when the microgrid n has surplus electric energy and lacks heat energy and the microgrid m has surplus heat energy and lacks electric energy, then the microgrid n and the microgrid m are paired for barter trade; the surplus electric energy of the microgrid n is transmitted to the microgrid m, and the surplus heat energy of the microgrid m is transmitted to the microgrid n, thereby realizing the energy complementation among microgrids.
6. The multi-energy complementary three-layer optimal operation method for a combined heat and power microgrid according to claim 1, wherein: In the step 2, the Floyd-Warshall algorithm is adopted to select the optimal path for energy trade among cogeneration microgrids, comprising the following steps: Step 2.1: Let N(V,A) be a CCHP microgrid connection network, where V = {1,2,3,…,n} is a set of CCHP microgrid nodes, and |V| = n; A = {(i,k): i,k∈V,i≠k} is a set of edges between two CCHP microgrids; i represents the ith CCHP; k represents the kth CCHP; Step 2.2: Setting D j , R j (j = 0, 1,..., n) is an n x n matrix in a combined heat and power microgrid connected network; j is the order, n is the total number of network nodes, wherein, D j is the path matrix, R j is the predecessor matrix; Step 2.3: When j = 0: At this time, D0= [d ik ] : D0 denotes the 0th order path matrix; d ik denotes the path distance from microgrid i to microgrid k; At this time, R0 = [r ik ] : R0 denotes a 0th order precursor matrix; r ik denotes an intermediate point on the shortest path from microgrid i to microgrid k; Step 2.4: When j = 1: At this time, D1 = [d ik ] D1 denotes the first order path matrix; d ij denotes the path distance between microgrid i and microgrid j, d jk denotes the path distance between microgrid j and microgrid k; At this time, R1= [r ik ] R1 represents a first order precursor matrix; r ik represents an intermediate point on the shortest path from microgrid i to microgrid k; Step 2.5: Stop until j = n, at this time the optimal transmission path between any two CCHP microgrids can be obtained.