Multi-microgrid intelligent power distribution system joint optimization operation method based on energy interaction

Abstract

The invention discloses a multi-microgrid intelligent power distribution system joint optimization operation method based on energy interaction, and the method comprises the following steps: 1), taking the minimum total operation cost of the power generation cost, the environment cost and the power exchange cost between the power generation cost and a power distribution network as a target, and building a bottom layer multi-microgrid system optimization model; 2) constructing an optimization model of an upper-layer intelligent power distribution system, considering the cost of power purchase of an upper-layer power grid, the cost of controllable distributed power generation, the cost of load demand response compensation and the cost of interaction with a multi-microgrid system, and establishing a model by taking the minimum total operation cost and the minimum payload peak-valley difference of the intelligent power distribution network as targets; 3) converting the upper-layer model and the lower-layer model into a second-order cone programming problem and a mixed integer linear programming problem respectively by using a second-order cone relaxation method and a linearization method to solve the two-order cone programming problem and the mixed integer linear programming problem; according to the invention, the operation cost of the intelligent power distribution system and the multi-microgrid system can be reduced, the load fluctuation condition of the intelligent power distribution system is reduced, and the electric energy quality of the intelligent power distribution system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systematization, and in particular to a method for joint optimization operation of a multi-microgrid intelligent distribution system based on energy interaction. Background Art

[0002] In the context of the construction of new power systems, new energy is expected to usher in new development opportunities. In addition to the traditional centralized large-scale new energy development mode, the intelligent distribution network with a high proportion of distributed new energy access will be an important way for the rapid development of new energy. Microgrids organically combine distributed power sources, energy storage systems and flexible loads, realize the intelligent interaction of sources, loads and storage, and effectively suppress the fluctuation of new energy power generation. It is an important way to promote the access of new energy to the power grid.

[0003] In recent years, with the development of microgrid technology and modern information and communication technology, more and more short-range microgrids have formed multi-microgrids. In the case of multi-microgrid coupling, realizing the optimal energy control of the intelligent distribution network is an important supporting technology to ensure the efficient utilization and economic operation of new energy. The microgrid energy management system occupies a pivotal position in microgrid energy optimization. The existing research on autonomous optimization and dispatching of single microgrids aims to ensure the economic and reliable operation of microgrids. With the continuous development of the concept of sharing economy, it has been rapidly promoted and applied in the power, communication and other industries. For the distribution system, energy sharing can improve the utilization efficiency of resources. Such measures can not only reduce the operating cost of the system, but also promote the better consumption of new energy such as photovoltaics.

[0004] However, existing research focuses on the energy interaction between microgrids and the main grid, and there is a relative lack of research on the energy exchange between microgrids. The load fluctuation during power exchange between multiple microgrids and the intelligent distribution system is not considered, and the quantitative analysis of the system operating cost is not in-depth enough. Summary of the invention

[0005] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a method for joint optimization operation of a multi-microgrid intelligent distribution system based on energy interaction, so as to reduce the operating costs of the intelligent distribution system and the multi-microgrid system, alleviate the load fluctuation of the intelligent distribution system, and improve the power quality of the intelligent distribution system.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for joint optimization operation of a multi-microgrid intelligent distribution system based on energy interaction, comprising the following steps:

[0007] Step 1), with the goal of minimizing the total operating cost of power generation cost, environmental cost and power exchange cost between it and the distribution network, establish an underlying multi-microgrid system optimization model;

[0008] Step 2) construct an optimization model for the upper-level intelligent distribution system, taking into account the cost of purchasing electricity from the upper-level power grid, the cost of generating electricity from controllable distributed power sources, the cost of load demand response compensation, and the cost of interacting with the multi-microgrid system, and establishing a model with the goal of minimizing the total operating cost and minimizing the peak-to-valley difference of the net load of the intelligent distribution network;

[0009] Step 3) Use the second-order cone relaxation method and linearization method to transform the upper and lower layer models into second-order cone programming and mixed integer linear programming problems for solution.

[0010] Preferably, in step 1): the underlying multi-microgrid system optimization model is:

[0011]

[0012] F i (P i t )=C fi (P i t )+C OMi (P i t )

[0013] C OMi (P i t )=K OMi (P i t )×P i t

[0014]

[0015] in, is the total operating cost of the lower optimization model; β1, β2, β3 are the total operating cost objective function and The weight coefficient satisfies β1+β2+β3=1; is the total power generation cost of multiple microgrids; F i (P i t ) is the power generation cost of each power unit at time t; C fi (P i t ) is the fuel cost of each micro-power source at time t; C OMi (P i t ) is the operation and maintenance cost of each micro power source at time t; K OMi is the maintenance factor of each micro-source; is the environmental management cost; γ jis the conversion factor of pollutant j; EP ij is the unit cost of pollutant j emitted by microgrid power source i; P i t is the power of microgrid power source i at time t; is the power exchange cost between multi-microgrids and smart distribution grid; is the price of electricity sold by the multi-microgrid to the smart distribution network in time period t, is the price of electricity purchased from the smart distribution network, is the corresponding power of electricity sold, is the corresponding power of purchased electricity.

[0016] Preferably, in step 2): the upper-level intelligent power distribution system optimization model is:

[0017] F=minC DSO &D R

[0018] C DSO =C grid +C NDG +C NDR +C tra

[0019]

[0020]

[0021] Among them, C DSO is the operating cost, D R is the difference between the peak and trough of the net load of the smart distribution network; C grid is the cost of purchasing electricity from the upper grid, C NDG is the controllable distributed generation cost, C NDR Compensation cost for load demand response, C tra The cost of interaction with multi-microgrid systems; represents the electricity purchase price of the smart distribution network from the upper power grid during period t, represents the power purchased by the smart distribution network from the upper grid during period t; a NMT , b NMT and c NMT are the power generation cost coefficients of micro-turbines, is the power generation of the micro-turbine in the smart distribution network during period t; NDR is the translation load compensation factor, and are the values ​​of the shiftable load before and after shifting in the smart distribution network in period t, respectively; is the net load of the smart distribution network during period t, and are the maximum and minimum values ​​of the net load curve of the smart distribution network, respectively, λ is the unit power network usage fee, P nm,t is the shared power between microgrid n and microgrid m during period t, is the power consumed by the transferable load at time t, and are the outputs of distributed photovoltaic and wind power in the distribution network during period t respectively.

[0022] Preferably, the specific process of step 3) is as follows:

[0023] The upper and lower models are respectively converted into second-order cone programming and mixed integer linear programming problems for solution using the second-order cone relaxation method and linearization method. The power sales and purchase scheduling of the lower microgrid is determined by the calculation results of the common connection point power by the upper optimization model. During the optimization solution process, if both the upper and lower models have solutions, the upper and lower optimization scheduling results are output respectively, and the solution is completed; if the upper model has no feasible solution, the upper and lower limits of the common connection point power are adjusted and then calculated again until the best feasible solution is found; the lower optimization model performs optimization calculations under the condition of constant common connection point power. If there is a solution, the optimization is completed and the final scheduling result is output. If there is no solution, the upper and lower limits of the common connection point power are further adjusted, and the optimization results are fed back to the upper model, and then the upper model is recalculated until the best feasible solution is found.

[0024] Beneficial effects of the present invention: Compared with the prior art, the present invention first establishes an optimization model for the underlying multi-microgrid system, optimizes the multi-microgrid system model with the goal of minimizing the total operating cost of power generation cost, environmental cost and power exchange cost between it and the intelligent distribution network, and the upper model aims to minimize the operating cost of the distribution system and minimize the net load peak-to-valley difference. Considering the cost of purchasing electricity from the upper power grid, the cost of power generation of controllable distributed power sources, the cost of load demand response compensation and the cost of interaction with the multi-microgrid system, the optimization model of the upper intelligent distribution system is established with the goal of minimizing the total operating cost and minimizing the net load peak-to-valley difference of the intelligent distribution network. Based on the second-order cone relaxation technology, the upper optimization model is converted into a linear model, and the underlying model is converted into a mixed integer linear programming problem for solution. By establishing energy interaction between the multi-microgrid and the intelligent distribution system and performing upper and lower layer optimization, the scheduling work of the multi-microgrid can be more effectively guided, the operating costs of the intelligent distribution system and the multi-microgrid system can be reduced, and the net load peak-to-valley difference of the distribution system can be alleviated. While reducing the operating costs of the intelligent distribution system and the multi-microgrid system, the load fluctuation of the intelligent distribution system is alleviated and the power quality of the intelligent distribution system is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flowchart of a method for joint optimization operation of a multi-microgrid intelligent power distribution system based on energy interaction of the present invention;

[0026] Figure 2 It is a flow chart for solving the upper and lower layer optimization models in the embodiment;

[0027] Figure 3 It is a schematic diagram of the structure of the IEEE-33 node power distribution system in the embodiment;

[0028] Figure 4 The optimized operation results of each controllable power source in the microgrid 1 obtained by solving the model in the embodiment;

[0029] Figure 5 The optimized operation results of each controllable power source in the microgrid 2 obtained by solving the model in the embodiment;

[0030] Figure 6 The optimized operation results of each controllable power source in the microgrid 3 obtained by solving the model in the embodiment;

[0031] Figure 7 It is a diagram showing the exchange power result between the microgrid and the smart distribution network in the embodiment;

[0032] Figure 8 This is a diagram showing the optimized output results of the intelligent power distribution system purchasing electricity from multiple microgrids, distributed wind power, distributed photovoltaic power and micro-gas turbines in the embodiment;

[0033] Fig. 9 It is the net load optimization result of the intelligent power distribution system in the embodiment. DETAILED DESCRIPTION

[0034] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0035] Example 1: Figure 1 As shown, a method for joint optimization operation of a multi-microgrid intelligent distribution system based on energy interaction includes the following steps:

[0036] Step 1) Establish an optimization model for the underlying multi-microgrid system with the goal of minimizing the total operating cost of power generation cost, environmental cost and power exchange cost between it and the distribution network;

[0037] Build a power generation cost model:

[0038]

[0039] F i (P i t )=C fi (P i t )+C OMi (P i t )

[0040] C OMi (P i t )=K OMi (P i t )×P i t

[0041] In the formula, is the total power generation cost of multiple microgrids; F i (P i t ) is the power generation cost of each power unit at time t; C fi (P i t ) is the fuel cost of each micro-power source at time t; C OMi (P i t ) is the operation and maintenance cost of each micro-source (including ESS) at time t; K OMi is the maintenance factor of each micro power source.

[0042] Building an environmental cost model

[0043]

[0044] In the formula, is the environmental management cost; γ j is the conversion factor of pollutant j; EP ij is the unit cost of pollutant j emitted by microgrid source i.

[0045] Modeling the power exchange cost:

[0046] The power exchange cost between multi-microgrids and smart distribution grid is as follows:

[0047]

[0048] In the formula, is the price of electricity sold by the multi-microgrid to the smart distribution network in time period t, is the price of electricity purchased from the smart distribution network, is the corresponding power of electricity sold, is the corresponding power of purchased electricity.

[0049] The total operating cost is:

[0050]

[0051] In the formula, is the total operating cost of the lower optimization model; β1, β2, β3 are the total operating cost objective function and The weight coefficient satisfies β1+β2+β3=1.

[0052] The system constraint model is specifically as follows:

[0053] Controllable generator set output constraints:

[0054]

[0055] In the formula, and The power generation capacity of micro gas turbines, photovoltaics and wind turbines, and are the upper and lower limits of the micro-turbine output respectively, and They are the upper and lower limits of photovoltaic power generation output in time period t respectively; and They are the upper and lower limits of fan output respectively.

[0056] Power balance constraints:

[0057]

[0058] In the formula, represents the power load of multiple microgrids in time t, and They represent the charging and discharging power of the energy storage system in the microgrid within time t respectively.

[0059] Energy storage battery constraints:

[0060] For ESS, the following operating constraints must be met:

[0061]

[0062] SOC min ≤SOC t ≤SOC max

[0063] When the ESS is discharging, that is, The remaining capacity is:

[0064]

[0065] When the ESS is charging, that is, The remaining capacity is:

[0066]

[0067] In the formula, is the minimum and maximum charge / discharge power of the ESS; SOC min , SOC maxare the upper and lower limits of ESS capacity respectively; η C represents the charging efficiency, η D Indicates discharge efficiency; Q ESS Indicates the rated capacity of the battery.

[0068] Step 2) Considering the cost of purchasing electricity from the upper power grid, the cost of generating electricity from controllable distributed generation, the cost of load demand response compensation, and the cost of interacting with the multi-microgrid system, an optimization model of the upper intelligent distribution system is established with the goal of minimizing the total operating cost and minimizing the peak-to-valley difference of the net load of the intelligent distribution network;

[0069] The minimum operating cost model of the intelligent power distribution system is as follows:

[0070] The cost of power purchased by the distribution network from the upper power grid is C grid for:

[0071]

[0072] in: and are the electricity purchase price and power purchased by the distribution network from the upper grid during period t respectively;

[0073] The cost of controllable distributed generation in intelligent power distribution system C NDG for:

[0074]

[0075] Among them: a NMT , b NMT and c NMT are the power generation cost coefficients of micro-turbines, is the power generation of the micro-turbine in the distribution network during period t;

[0076] Distribution network load demand response compensation cost C NDR for:

[0077]

[0078] Where: β NDR is the translation load compensation factor, and They are the values ​​of the shiftable load in the distribution network before and after the shift in period t respectively.

[0079] The interaction cost C between the distribution network and the multi-microgrid system tra for:

[0080]

[0081] Total cost of distribution network operation C DSO for:

[0082] C DSO =C grid +C NDG +C NDR +C tra

[0083] The model for minimizing the peak-to-valley difference of the payload of the smart distribution network (DN) is as follows:

[0084]

[0085] in: is the net load of the distribution network during period t, and are the maximum and minimum values ​​of the net load curve of the distribution network, respectively. R It is the peak-to-valley difference of the net load curve of the distribution network.

[0086] The system constraint model is specifically as follows:

[0087] Power balance constraints:

[0088]

[0089] Distributed photovoltaic output constraints:

[0090] in: and They are the upper and lower limits of distributed photovoltaic output in the distribution network respectively.

[0091] Distributed wind power output constraints:

[0092]

[0093] in: and They are the upper and lower limits of distributed wind power output in the distribution network respectively.

[0094] Micro turbine output constraints:

[0095]

[0096] in: and They are the upper and lower limits of distributed wind power output in the distribution network respectively.

[0097] Translational load constraints:

[0098]

[0099] in: and They are the upper and lower limits of the load that can be shifted in the distribution network.

[0100] Line flow constraints:

[0101] P l min ≤P l t ≤P l max

[0102] Where: P l max and P l min are the upper and lower limits of the power flow of distribution line l respectively.

[0103] The optimization model of the intelligent distribution system is expressed as:

[0104] F=minC DSO &D R

[0105] Step 3) using the second-order cone relaxation method and linearization method to transform the upper and lower layer models into second-order cone programming and mixed integer linear programming problems for solution;

[0106] The second-order cone relaxation method is used to transform the power flow equation of the smart distribution network system containing multiple microgrids and its constraints or inequality constraints with other related equations into linear second-order cone programming equations for solution. The lower optimization model involves the calculation and optimization operation of the output range. Its nonlinear part is processed by piecewise linearization. After being converted into mixed integer linear programming, it is quickly solved by using Matlab to call CPLEX solver. The power sales and purchase scheduling of the lower microgrid is determined by the calculation results of the power of the common connection point by the upper optimization model. During the optimization and solution process, if both the upper and lower models have solutions, the upper and lower optimization scheduling results are output respectively, and the solution is completed. If there is no feasible solution for the upper model, the upper and lower limits of the power of the common connection point are adjusted and then calculated again until the best feasible solution is found. The lower optimization model performs optimization calculation under the condition of constant power of the common connection point. If there is a solution, the optimization ends and the final scheduling result is output. If there is no solution, the upper and lower limits of the power of the common connection point are adjusted again, and the optimization result is fed back to the upper model, and then the calculation is re-started from the upper model until the best feasible solution is found. The solution process of the upper and lower optimization models is as follows: Figure 2 shown.

[0107] The present invention selects the standard example IEEE-33 node distribution system, and improves it on this basis, and performs algorithm analysis on the intelligent distribution network system containing multiple microgrids. The system includes three microgrids MG1, MG2 and MG3, each of which contains FC, PV, WT, MT, ESS, DE and node loads. This model is used as a test network for simulation verification. The structural diagram of the IEEE-33 node distribution system is shown in Figure 3 The controllable power supply parameters are shown in the following table:

[0108]

[0109] The parameters of the intelligent power distribution system are shown in the following table:

[0110]

[0111] The solution method of step 3) is used to solve the two-layer optimization scheduling model to obtain the optimal operation results of each controllable power supply in the three microgrids under the grid connection condition. Figure 4 , 5 As shown in Figure 6, it can be seen that the load of the microgrid is mainly met by controllable fuel units and distributed new energy sources, which can absorb power from the intelligent distribution system and provide power for the intelligent distribution system. The load demand of the three microgrids increases in turn. The ESS charges at low loads and discharges at peak times. It maintains power balance through charging and discharging, and maximizes the use of electricity prices during electricity consumption time to reduce their respective operating costs. The microgrid transmits excess power to the intelligent distribution network, which greatly alleviates the burden of the power grid at peak loads and plays a good role in shaving peaks and filling valleys to achieve the smooth operation of the intelligent distribution network. At the same time, due to the characteristics of low maintenance costs and low pollution emissions, the economic and environmental costs of its operation are the lowest. Figure 7 The power exchange between the smart distribution network and the microgrid, that is, the power at the public connection point, is shown. It can be seen that when the microgrid load is lower than the power generation power, the power at the public connection point is positive. At this time, the microgrid is equivalent to the generator having excess power to sell electricity to the smart distribution network system; when the microgrid load is large, the microgrid is equivalent to the load, and purchases electricity from the smart distribution network system through the public connection point. The power output of the public connection point can reduce the output of other units and is used to coordinate the power flow balance of the network, so that it can maximize the use of distributed energy.

[0112] Figure 8 The results of solving the double-layer optimization scheduling model show the optimized output of the intelligent distribution system purchasing electricity from multiple microgrids, distributed wind power, distributed photovoltaics and micro-turbines. During the daily optimization operation of the intelligent distribution system, the load demand is mainly supplied by the output of each unit. During the period of abundant renewable energy, the power curve of the distribution system purchasing electricity from multiple microgrids shows a clear downward trend. During the period of exhaustion of renewable energy output, the power curve of electricity purchase shows an upward trend. Fig. 9 is the net load optimization result of the intelligent distribution system in the embodiment. Scenario 1 is the result without considering the power interaction between multi-microgrids and the intelligent distribution system, and scenario 2 is the result after considering the energy interaction between them. It can be seen that considering the load demand response can reduce the load peak of the intelligent distribution system (such as the time period 17:00-20:00); on this basis, comprehensively considering the load demand response and the power exchange between the intelligent distribution system and the multi-microgrid system can further reduce the net load peak-to-valley difference of the system.

[0113] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for joint optimization operation of a multi-microgrid intelligent distribution system based on energy interaction, characterized in that: The following steps are involved: Step 1), with the goal of minimizing the total operating cost of power generation cost, environmental cost and power exchange cost between it and the distribution network, establish an underlying multi-microgrid system optimization model; Step 2) construct an optimization model for the upper-level intelligent distribution system, taking into account the cost of purchasing electricity from the upper-level power grid, the cost of generating electricity from controllable distributed power sources, the cost of load demand response compensation, and the cost of interacting with the multi-microgrid system, and establishing a model with the goal of minimizing the total operating cost and minimizing the peak-to-valley difference of the net load of the intelligent distribution network; Step 3) Use the second-order cone relaxation method and linearization method to transform the upper and lower layer models into second-order cone programming and mixed integer linear programming problems for solution.

2. According to claim 1, a method for joint optimization operation of a multi-microgrid intelligent distribution system based on energy interaction is characterized in that: In step 1), the underlying multi-microgrid system optimization model is: F i (P i t )=C fi (P i t )+C OMi (P i t ) C OMi (P i t )=K OMi (P i t )×P i t in, is the total operating cost of the lower optimization model; β1, β2, β3 are the total operating cost objective function and The weight coefficient satisfies β1+β2+β3=1; is the total power generation cost of multiple microgrids; F i (P i t ) is the power generation cost of each power unit at time t; C fi (P i t ) is the fuel cost of each micro-power source at time t; C OMi (P i t ) is the operation and maintenance cost of each micro power source at time t; K OMi is the maintenance factor of each micro-source; is the environmental management cost; γ j is the conversion factor of pollutant j; EP ij is the unit cost of pollutant j emitted by microgrid power source i; P i t is the power of microgrid power source i at time t; is the power exchange cost between multi-microgrids and smart distribution grid; is the price of electricity sold by the multi-microgrid to the smart distribution network in time period t, is the price of electricity purchased from the smart distribution network, is the corresponding power of electricity sold, is the corresponding power of purchased electricity.

3. The method for joint optimization operation of a multi-microgrid intelligent power distribution system based on energy interaction according to claim 1 is characterized in that: In step 2), the upper-level intelligent power distribution system optimization model is: F=minC DSO &D R C DSO =C grid +C NDG +C NDR +C tra Among them, C DSO is the operating cost, D R is the difference between the peak and trough of the net load of the smart distribution network; C grid is the cost of purchasing electricity from the upper grid, C NDG is the controllable distributed generation cost, C NDR Compensation cost for load demand response, C tra The cost of interaction with multi-microgrid systems; represents the electricity purchase price of the smart distribution network from the upper power grid during period t, represents the power purchased by the smart distribution network from the upper grid during period t; a NMT 、b NMT and c NMT are the power generation cost coefficients of micro-turbines, is the power generation of the micro-turbine in the smart distribution network during period t; NDR is the translation load compensation factor, and are the values ​​of the shiftable load before and after shifting in the smart distribution network in period t, respectively; is the net load of the smart distribution network during period t, and are the maximum and minimum values ​​of the net load curve of the smart distribution network, respectively, λ is the unit power network usage fee, P nm,t is the shared power between microgrid n and microgrid m during period t, is the power consumed by the transferable load at time t, and are the outputs of distributed photovoltaic and wind power in the distribution network during period t respectively.

4. The method for joint optimization operation of a multi-microgrid intelligent power distribution system based on energy interaction according to claim 1 is characterized in that: Step 3) The specific process is as follows: The upper and lower models are respectively converted into second-order cone programming and mixed integer linear programming problems for solution using the second-order cone relaxation method and linearization method. The power sales and purchase scheduling of the lower microgrid is determined by the calculation results of the common connection point power by the upper optimization model. During the optimization solution process, if both the upper and lower models have solutions, the upper and lower optimization scheduling results are output respectively, and the solution is completed; if the upper model has no feasible solution, the upper and lower limits of the common connection point power are adjusted and then calculated again until the best feasible solution is found; the lower optimization model performs optimization calculations under the condition of constant common connection point power. If there is a solution, the optimization is completed and the final scheduling result is output. If there is no solution, the upper and lower limits of the common connection point power are further adjusted, and the optimization results are fed back to the upper model, and then the upper model is recalculated until the best feasible solution is found.

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