Multi-resource micro-grid optimal scheduling method, device and equipment and storage medium

Abstract

The invention discloses a multi-resource micro-grid optimal scheduling method, device and equipment and a storage medium, and aims at minimizing the total cost of a multi-resource micro-grid, constructing a target function, solving a robust feasible region of the target function in each selected time period based on the constraint condition of the operation of the multi-resource micro-grid, and optimizing the optimal scheduling of the multi-resource micro-grid. The robust feasible region of the target function in the selected time period is taken as an additional constraint, an optimization algorithm is adopted to solve an optimal scheduling strategy in each selected time period, the optimal scheduling strategies in each selected time period are integrated to obtain a global optimal solution, and optimal distribution of the multi-resource microgrid is realized, so that an optimization target with the lowest comprehensive cost is realized, and the optimal scheduling effect of the multi-resource microgrid is improved. And the energy efficiency is improved.

Description

Technical Field

[0001] The present invention relates to power grid dispatching technology, and in particular to a multi-resource microgrid optimization dispatching method, device, equipment and storage medium. Background Art

[0002] In recent years, the proportion of renewable energy power generation in total power generation has been increasing. However, renewable energy such as photovoltaic power generation is greatly affected by the alternation of day and night and changes in weather conditions. It has the characteristics of volatility, randomness and intermittency, which seriously limits the penetration and regulation capabilities of renewable energy in the power system, resulting in the inability to reasonably dispatch grid resources. Summary of the invention

[0003] The present invention provides a multi-resource microgrid optimization scheduling method, device, equipment and storage medium to achieve optimal allocation of multi-resource microgrids and improve energy efficiency.

[0004] In a first aspect, the present invention provides a multi-resource microgrid optimization scheduling method, comprising:

[0005] Taking the minimization of the total cost of the multi-resource microgrid as the goal, the objective function is constructed;

[0006] Based on the constraints of the operation of the multi-resource microgrid, solving the robust feasible region of the objective function in each selected time period;

[0007] Taking the robust feasible region of the objective function in the selected time period as an additional constraint, an optimization algorithm is used to solve the optimal scheduling strategy in each selected time period;

[0008] The optimal dispatching strategies of each selected time period are integrated to obtain a global optimal solution, thereby achieving optimal allocation of the multi-resource microgrid.

[0009] Optionally, the objective function is:

[0010] min G=M grid +M turbine +M solar +M hydro +M storage +M punish

[0011] Among them, M grid , M turbine , M solar , M hydro , M storage , M punish They represent the power procurement cost of the power grid, the consumption cost of the gas turbine, the maintenance cost of photovoltaic power generation, the maintenance cost of hydropower generation, the operation cost of the energy storage system, and the penalty cost caused by environmental factors;

[0012] The electricity procurement cost is as follows:

[0013]

[0014] Where m buy,t is the electricity price of the relaxed node at time t, P 0,t represents the active power flowing into the node from the power grid, Δt is the time period of one hour, and T is the entire time period;

[0015] The consumption cost of gas turbine is as follows:

[0016]

[0017] Where x, y, z are cost coefficients, P GST,k,k is the power generation of the kth gas turbine at time t, W is the total number of gas turbines;

[0018] The maintenance cost of photovoltaic power generation is as follows:

[0019]

[0020] Among them, ξ PV is the unit operation and maintenance cost of the photovoltaic power station, is the photovoltaic power output of the kth photovoltaic power station at time t, C pv is the total number of photovoltaic power stations;

[0021] The maintenance cost of hydropower generation is as follows:

[0022]

[0023] Among them, n M,k represents the operation and maintenance cost per unit power of k equipment, represents the output power of device k;

[0024] The operating cost of the energy storage system is as follows:

[0025]

[0026] Among them, m storage is the energy storage cost coefficient, is the input power of the k energy storage system at time k, is the charging power coefficient, is the output power of the k energy storage system at time k, is the output power coefficient;

[0027] The penalty cost is as follows:

[0028]

[0029] Among them, mpunish,f is the penalty cost of wind curtailment, m punish,y is the penalty deviation coefficient, is the amount of wind power abandoned by wind turbine k at time t, U l,t is the voltage value of node l at time t, U refer is the node voltage reference value.

[0030] Optionally, solving the robust feasible region of the objective function in each selected time period includes:

[0031] The objective function is rewritten in a compact form as follows:

[0032]

[0033] in, is the heat storage at time t, m t is the decision coefficient, h t is an equivalent variable, b t are independent decision variables, c t is the time constant, is the uncertain variable of new energy power output, M, N, K, L are weight coefficients;

[0034] Right t The constructed uncertainty set is as follows:

[0035] D t ={d t |0≤d t ≤d max}

[0036] Among them, d max The maximum power output of wind power generation;

[0037] The relevance of decision variables is defined as follows:

[0038]

[0039] The robust feasible region of each selected time period is defined as G t ,but

[0040]

[0041] G t =[G1,G2,…,G t-1 ]

[0042]

[0043] Among them, A m is the upper bound of the state function, A n is the lower bound of the state function, is the upper bound of energy storage output, is the lower bound of energy storage output, P GTS,min is the minimum output power of the gas turbine, P GTS,maz is the maximum output power of the gas turbine, is the upper limit of photovoltaic power output, is the lower bound of photovoltaic power output.

[0044] Optionally, the multi-resource microgrid optimization scheduling method further includes: rewriting the gas turbine consumption cost function by using binary component linearization as follows:

[0045]

[0046] in, is the gas turbine cost coefficient, is the output power of the gas turbine at node k at time t.

[0047] Optionally, the multi-resource microgrid optimization scheduling method further includes: rewriting the penalty cost function by using binary component linearization as:

[0048]

[0049] Among them, λ l,t is the voltage change.

[0050] Optionally, the constraints for the operation of the multi-resource microgrid include:

[0051] (1) Active and reactive power balance constraints:

[0052]

[0053]

[0054] Among them, P k,t is the active power of line k at time t, P l,t is the active power of line l at time t, p p,t is the load active power; Q k,t is the reactive power of line k at time t, Q l,t is the reactive power of line l at time t, Q p,t is the reactive power of the load;

[0055] (2) Voltage amplitude constraint:

[0056]

[0057] in, is the voltage of node j of line k, is the voltage of node j in line l, U j,tis the voltage of node j at time t;

[0058] (3) Gas turbine operation constraints:

[0059]

[0060] in, is the minimum output power of the gas turbine at node k, P GST,k,t is the output power of node k at time t, is the maximum output power of the gas turbine at node k;

[0061] (4) PV power station output power constraints:

[0062]

[0063] in, is the minimum output power of the photovoltaic power station, is the maximum output power of the photovoltaic power station;

[0064] (5) Hydropower station operation constraints:

[0065]

[0066] in, is the minimum output power of the hydropower station, is the maximum output power of the hydropower station;

[0067] (6) Energy constraints of energy storage system:

[0068]

[0069] in, is the charging power of node i at time t, is the discharge power of node i at time t, P i gt is the peak power.

[0070] Optionally, the robust feasible region of the objective function in the selected time period is used as an additional constraint, and an optimization algorithm is used to solve the optimal scheduling strategy in each selected time period, including:

[0071] Taking the robust feasible region of the objective function in the selected time period as an additional constraint, the minimization problem of the objective function is transformed into an optimal solution problem;

[0072] The raccoon optimization algorithm is used to solve the optimal solution problem and obtain the optimal scheduling strategy in each selected time period.

[0073] In a second aspect, the present invention further provides a multi-resource microgrid optimization scheduling device, comprising:

[0074] An objective function building module is used to build an objective function with the goal of minimizing the total cost of the multi-resource microgrid;

[0075] A feasible domain solving module, used for solving the robust feasible domain of the objective function in each selected time period based on the constraints of the operation of the multi-resource microgrid;

[0076] An optimal scheduling strategy solving module is used to solve the optimal scheduling strategy in each selected time period using an optimization algorithm with the robust feasible region of the objective function in the selected time period as an additional constraint;

[0077] The global optimal solution integration module is used to integrate the optimal dispatching strategies of each selected time period to obtain the global optimal solution and realize the optimal allocation of the multi-resource microgrid.

[0078] In a third aspect, the present invention further provides an electronic device, comprising:

[0079] one or more processors;

[0080] A storage device for storing one or more programs;

[0081] When the one or more programs are executed by the one or more processors, the one or more processors implement the multi-resource microgrid optimization scheduling method provided in the first aspect of the present invention.

[0082] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-resource microgrid optimization scheduling method provided in the first aspect of the present invention.

[0083] The multi-resource microgrid optimization scheduling method provided by the present invention takes the minimization of the total cost of the multi-resource microgrid as the goal, constructs an objective function, solves the robust feasible domain of the objective function in each selected time period based on the constraints of the operation of the multi-resource microgrid, takes the robust feasible domain of the objective function in the selected time period as an additional constraint, adopts an optimization algorithm to solve the optimal scheduling strategy in each selected time period, integrates the optimal scheduling strategy of each selected time period, obtains a global optimal solution, realizes the optimal allocation of the multi-resource microgrid, thereby achieving the optimization goal of lowest comprehensive cost and improving energy efficiency.

[0084] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0086] Figure 1 A flowchart of a multi-resource microgrid optimization scheduling method provided by the present invention;

[0087] Figure 2 A schematic diagram of the structure of a multi-resource microgrid provided by the present invention;

[0088] Figure 3 A structural schematic diagram of a multi-resource microgrid optimization scheduling device is provided for the present invention;

[0089] Figure 4 The present invention provides a schematic structural diagram of an electronic device.

[0090] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0091] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0092] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0093] Figure 1This is a flow chart of a multi-resource microgrid optimization scheduling method provided by the present invention. This embodiment can be applied to optimize the scheduling of a multi-resource microgrid. The method can be executed by a multi-resource microgrid optimization scheduling device provided by an embodiment of the present invention. The device can be implemented by software and / or hardware, and is usually configured in an electronic device, such as Figure 1 As shown, the multi-resource microgrid optimization scheduling method includes the following steps:

[0094] S101. Construct an objective function with the goal of minimizing the total cost of the multi-resource microgrid.

[0095] Figure 2 A schematic diagram of the structure of a multi-resource microgrid provided by the present invention is shown in FIG. Figure 2 As shown, the multi-resource microgrid includes a power grid, photovoltaics, hydropower, a gas turbine, a thermal storage system, an energy storage system and a variable load. The power grid, photovoltaics, hydropower, a gas turbine, an energy storage system and a variable load are all connected to the power bus, the gas turbine, the thermal storage system and the variable load are connected to the thermal bus, and the microgrid control system is connected to the power bus and the thermal bus.

[0096] In an embodiment of the present invention, for a multi-resource microgrid including photovoltaics, hydropower, etc., an objective function is constructed with the goal of minimizing the total cost of the multi-resource microgrid. Taking into account the influence of renewable energy on environmental factors such as weather, a penalty cost is introduced to balance energy supply and operating economy.

[0097] Exemplarily, the objective function is:

[0098] min G=M grid +M turbine +M solar +M hydro +M storage +M punish

[0099] Among them, M grid , M turbine , M solar , M hydro , M storage , M punish They represent the power procurement cost of the power grid, the consumption cost of the gas turbine, the maintenance cost of photovoltaic power generation, the maintenance cost of hydropower generation, the operation cost of the energy storage system, and the penalty cost caused by environmental factors;

[0100] The electricity procurement cost is as follows:

[0101]

[0102] Where m buy,t is the electricity price of the relaxed node at time t, P 0,trepresents the active power flowing into the node from the power grid, Δt is the time period of one hour, and T is the entire time period;

[0103] The consumption cost of gas turbine is as follows:

[0104]

[0105] Where x, y, z are cost coefficients, P GST,k,t is the power generation of the kth gas turbine at time t, W is the total number of gas turbines;

[0106] The maintenance cost of photovoltaic power generation is as follows:

[0107]

[0108] Among them, ξ PV is the unit operation and maintenance cost of the photovoltaic power station, is the photovoltaic power output of the kth photovoltaic power station at time t, C pv is the total number of photovoltaic power stations;

[0109] The maintenance cost of hydropower generation is as follows:

[0110]

[0111] Among them, m M,k represents the operation and maintenance cost per unit power of k equipment, represents the output power of device k;

[0112] The operating cost of the energy storage system is as follows:

[0113]

[0114] Among them, m storage is the energy storage cost coefficient, is the input power of the k energy storage system at time t, is the charging power coefficient, is the output power of the k energy storage system at time t, is the output power coefficient;

[0115] The penalty cost is as follows:

[0116]

[0117] Among them, m punish,f is the penalty cost of wind curtailment, m punish,y is the penalty deviation coefficient, is the amount of wind power abandoned by wind turbine k at time t, U l,t is the voltage value of node l at time t, U refer is the node voltage reference value.

[0118] In some embodiments of the present invention, considering that the functional expressions of the gas turbine consumption cost and the penalty cost are nonlinear, the functional expression of the gas turbine consumption cost is linearized by using binary component linearization, and a binary variable t=[t1, t2, …, t n ], and divide its nonlinear function into n segments for solution. The results are as follows:

[0119]

[0120] in, is the gas turbine cost coefficient, is the output power of the gas turbine at node k at time t.

[0121] Similarly, the penalty cost function is rewritten by linearizing the binary component as follows:

[0122]

[0123] Among them, λ l,t is the voltage change.

[0124] S102. Based on the operating constraints of the multi-resource microgrid, solve the robust feasible region of the objective function in each selected time period.

[0125] In an embodiment of the present invention, based on the constraints of the operation of the multi-resource microgrid, the robust feasible region of the objective function in each selected time period is solved.

[0126] Among them, the constraints of multi-resource microgrid operation include:

[0127] (1) Active and reactive power balance constraints:

[0128]

[0129] Among them, P k,t is the active power of line k at time t, P l,t is the active power of line l at time t, P p,t is the load active power; Q k,t is the reactive power of line k at time t, Q l,t is the reactive power of line l at time t, Q p,t is the reactive power of the load;

[0130] (2) Voltage amplitude constraint:

[0131]

[0132] in, is the voltage of node j of line k, is the voltage of node j in line l, U j,tis the voltage of node j at time t;

[0133] (3) Gas turbine operation constraints:

[0134]

[0135] in, is the minimum output power of the gas turbine at node k, P GST,k,t is the output power of node k at time t, is the maximum output power of the gas turbine at node k;

[0136] (4) PV power station output power constraints:

[0137]

[0138] in, is the minimum output power of the photovoltaic power station, is the maximum output power of the photovoltaic power station;

[0139] (5) Hydropower station operation constraints:

[0140]

[0141] in, is the minimum output power of the hydropower station, is the maximum output power of the hydropower station;

[0142] (6) Energy constraints of energy storage system:

[0143]

[0144] in, is the charging power of node i at time t, is the discharge power of node i at time t, P i gt is the peak power.

[0145] For example, the objective function is rewritten in a compact form as follows:

[0146]

[0147] in, is the heat storage at time t, m t is the decision coefficient, h t is an equivalent variable, b t are independent decision variables, c t is the time constant, is the uncertain variable of new energy power output, M, N, K, L are weight coefficients;

[0148] Right t The constructed uncertainty set is as follows:

[0149] D t ={d t |0≤d t ≤d max}

[0150] Among them, d max The maximum power output of wind power generation;

[0151] The relevance of decision variables is defined as follows:

[0152]

[0153] The robust feasible region of each selected time period is defined as G t ,but

[0154]

[0155] G t =[G1,G2,…,G t-1 ]

[0156]

[0157] Among them, A m is the upper bound of the state function, A n is the lower bound of the state function, is the upper bound of energy storage output, is the lower bound of energy storage output, P GTS,min is the minimum output power of the gas turbine, P GTS,max is the maximum output power of the gas turbine, is the upper limit of photovoltaic power output, is the lower bound of photovoltaic power output.

[0158] S103, taking the robust feasible region of the objective function in the selected time period as an additional constraint, an optimization algorithm is used to solve the optimal scheduling strategy in each selected time period.

[0159] In the embodiment of the present invention, the robust feasible region of the objective function in the selected time period is taken as an additional constraint, and an optimization algorithm is used to solve the optimal scheduling strategy in each selected time period.

[0160] Exemplarily, the robust feasible domain of the objective function in the selected time period is used as an additional constraint, the minimization problem of the objective function is converted into an optimal solution problem, and the raccoon optimization algorithm is used to solve the optimal solution problem to obtain the optimal scheduling strategy in each selected time period. It should be noted that in other embodiments of the present invention, other optimization algorithms may also be used, and the embodiments of the present invention are not limited here.

[0161] The raccoon optimization algorithm is described as follows:

[0162] Coati Optimization Algorithm (COA) derives patterns from the behavior of coatis, including their aggressive behavior toward hyenas and their avoidance of predators.

[0163] The mathematical formula of the algorithm is as follows:

[0164] D i :d i,j =kh j +t(ph j -kh j ),i=1,2,...,m,j=1,2,...n

[0165] Where D i represents the position of the i-th raccoon in the search space, d i,j represents the value of the j-th decision variable, n represents the number of iterations, m represents the factor selection, t represents a random number between 0 and 1, and kh j and ph j represent the upper and lower bounds respectively.

[0166] The coati population is quantified by the following population matrix:

[0167] D=[d ij ] n×m

[0168] By analyzing the possible solutions in the decision variables, we can get Y = [Y (D i )] n×1 To represent the value of the objective function.

[0169] The algorithm can iteratively find the optimal solution by solving the two stages of exploration and development. By applying it to the solution of the optimal scheduling strategy, the optimal scheduling plan at time t can be obtained, thereby achieving optimal scheduling of renewable energy such as photovoltaics and hydropower.

[0170] The specific solution method is as follows:

[0171] (1) Exploration phase

[0172] Simulate the hunting process of the coati on the iguana. The coati climbs up the tree to intimidate the iguana. The coati under the tree waits for the iguana to fall and completes the capture of the iguana. The position movement of the coati can be expressed by the following formula:

[0173]

[0174] in, represents the updated position of the i-th raccoon, represents its j-th layer dimension, R j represents the j-th dimension of the hierarchy, and U represents a random number between 1 and 2.

[0175] The position of the raccoon on the ground is a random search area. The following formula describes the search space area of ​​the ground raccoon:

[0176] R P :

[0177]

[0178] Among them, R P represents the random ground position of the lizard, represents the position of the hyena in the jth dimension, represents the function value at the corresponding position, and [*] represents the largest integer less than or equal to the * value.

[0179] The update conditions for i=1,2,…,M are as follows:

[0180]

[0181] Among them, W i Q1 Represents the numerical value of the raccoon objective function.

[0182] (2) Development stage

[0183] The raccoon capture behavior is replicated and the raccoon position is randomly generated by the following formula.

[0184]

[0185] in, represents the new position of the raccoon calculated for the i-th time, represents the j-th dimension of the raccoon's position, t represents a random number between 0 and 1, l represents the number of iterations, and They represent the relative upper and lower bounds of the j-th decision parameter, respectively, and kh j and ph j Represent the upper and lower bounds of the parameter respectively.

[0186] If the objective function value increases, the estimated position is updated as follows:

[0187]

[0188] Among them, W i Q2 Represents the value of the raccoon objective function.

[0189] Through the above optimization algorithm, the optimal solution of the optimization problem can be obtained as The scheduling plan in each time period is The cost during this period is The state variables during this period are Based on this state variable, the scheduling plan for the next stage can be solved in combination with the raccoon optimization algorithm, so that the optimal scheduling plan for the entire optimization problem can be obtained.

[0190] S104: Integrate the optimal dispatching strategies for each selected time period to obtain a global optimal solution, thereby achieving optimal allocation of multi-resource microgrids.

[0191] By integrating the optimal scheduling plans within each selected time period, the optimal solution sets obtained by iterative raccoon algorithm within each selected time period are listed separately, and the global optimal solution can be obtained by comparison, thereby obtaining the optimal allocation plan for each resource of the microgrid considering photovoltaic and hydropower, and achieving the optimization goal of minimizing the comprehensive operating cost of the microgrid.

[0192] The multi-resource microgrid optimization scheduling method provided by the present invention takes the minimization of the total cost of the multi-resource microgrid as the goal, constructs an objective function, solves the robust feasible domain of the objective function in each selected time period based on the constraints of the operation of the multi-resource microgrid, takes the robust feasible domain of the objective function in the selected time period as an additional constraint, adopts an optimization algorithm to solve the optimal scheduling strategy in each selected time period, integrates the optimal scheduling strategy of each selected time period, obtains a global optimal solution, realizes the optimal allocation of the multi-resource microgrid, thereby achieving the optimization goal of lowest comprehensive cost and improving energy efficiency.

[0193] Figure 3 The present invention provides a structural schematic diagram of a multi-resource microgrid optimization scheduling device, such as Figure 3 As shown, the multi-resource microgrid optimization dispatching device includes:

[0194] An objective function construction module 201 is used to construct an objective function with the minimum total cost of the multi-resource microgrid as the goal;

[0195] A feasible domain solving module 202 is used to solve the robust feasible domain of the objective function in each selected time period based on the constraints of the operation of the multi-resource microgrid;

[0196] The optimal scheduling strategy solving module 203 is used to solve the optimal scheduling strategy in each selected time period using the robust feasible region of the objective function in the selected time period as an additional constraint using an optimization algorithm;

[0197] The global optimal solution integration module 204 is used to integrate the optimal dispatching strategies of each selected time period to obtain a global optimal solution and achieve optimal allocation of the multi-resource microgrid.

[0198] In some embodiments of the present invention, the objective function is:

[0199] min G=M grid +M turbine +M solar +M hydro +M storage +M punish

[0200] Among them, M grid , M turbine , M solar , M hydro , M storage , M punish They represent the power procurement cost of the power grid, the consumption cost of the gas turbine, the maintenance cost of photovoltaic power generation, the maintenance cost of hydropower generation, the operation cost of the energy storage system, and the penalty cost caused by environmental factors;

[0201] The electricity procurement cost is as follows:

[0202]

[0203] Where m buy,t is the electricity price of the relaxed node at time t, P 0,t represents the active power flowing into the node from the power grid, Δt is the time period of one hour, and T is the entire time period;

[0204] The consumption cost of gas turbine is as follows:

[0205]

[0206] Where x, y, z are cost coefficients, P GST,k,t is the power generation of the kth gas turbine at time t, W is the total number of gas turbines;

[0207] The maintenance cost of photovoltaic power generation is as follows:

[0208]

[0209] Among them, ξ PV is the unit operation and maintenance cost of the photovoltaic power station, is the photovoltaic power output of the kth photovoltaic power station at time t, C pv is the total number of photovoltaic power stations;

[0210] The maintenance cost of hydropower generation is as follows:

[0211]

[0212] Among them, m M,k represents the operation and maintenance cost per unit power of k equipment, represents the output power of device k;

[0213] The operating cost of the energy storage system is as follows:

[0214]

[0215] Among them, m storage is the energy storage cost coefficient, is the input power of the k energy storage system at time t, is the charging power coefficient, is the output power of the k energy storage system at time k, is the output power coefficient;

[0216] The penalty cost is as follows:

[0217]

[0218] Among them, m punish,f is the penalty cost of wind curtailment, m punish,y is the penalty deviation coefficient, is the amount of wind power abandoned by wind turbine k at time t, U l,t is the voltage value of node l at time t, U refer is the node voltage reference value.

[0219] In some embodiments of the present invention, solving the robust feasible region of the objective function in each selected time period includes:

[0220] The objective function is rewritten in a compact form as follows:

[0221]

[0222] in, is the heat storage at time t, m t is the decision coefficient, h t is an equivalent variable, b t are independent decision variables, c t is the time constant, is the uncertain variable of new energy power output, M, N, K, L are weight coefficients;

[0223] Right t The constructed uncertainty set is as follows:

[0224] D t ={d t |0≤d t≤d max}

[0225] Among them, d max The maximum power output of wind power generation;

[0226] The relevance of decision variables is defined as follows:

[0227]

[0228] The robust feasible region of each selected time period is defined as G t ,but

[0229]

[0230] G t =[G1,G2,…,G t-1 ]

[0231]

[0232] Among them, A m is the upper bound of the state function, A n is the lower bound of the state function, is the upper bound of energy storage output, is the lower bound of energy storage output, P GTS,min is the minimum output power of the gas turbine, P GTS,max is the maximum output power of the gas turbine, is the upper limit of photovoltaic power output, is the lower bound of photovoltaic power output.

[0233] In some embodiments of the present invention, the multi-resource microgrid optimization scheduling device further includes:

[0234] The first function rewriting module is used to rewrite the gas turbine consumption cost function into:

[0235]

[0236] in, is the gas turbine cost coefficient, is the output power of the gas turbine at node k at time t.

[0237] In some embodiments of the present invention, the multi-resource microgrid optimization scheduling device further includes:

[0238] The second function rewriting module is used to rewrite the penalty cost function into:

[0239]

[0240] Among them, λ l,t is the voltage change.

[0241] In some embodiments of the present invention, the constraints for the operation of the multi-resource microgrid include:

[0242] (1) Active and reactive power balance constraints:

[0243]

[0244] Among them, P k,t is the active power of line k at time t, P l,t is the active power of line l at time t, P p,t is the load active power; Q k,t is the reactive power of line k at time t, Q l,t is the reactive power of line l at time t, Q p,t is the reactive power of the load;

[0245] (2) Voltage amplitude constraint:

[0246]

[0247] in, is the voltage of node j of line k, is the voltage of node j in line l, U j,t is the voltage of node j at time t;

[0248] (3) Gas turbine operation constraints:

[0249]

[0250] in, is the minimum output power of the gas turbine at node k, P GST,k,t is the output power of node k at time t, is the maximum output power of the gas turbine at node k;

[0251] (4) PV power station output power constraints:

[0252]

[0253] in, is the minimum output power of the photovoltaic power station, is the maximum output power of the photovoltaic power station;

[0254] (5) Hydropower station operation constraints:

[0255]

[0256] in, is the minimum output power of the hydropower station, is the maximum output power of the hydropower station;

[0257] (6) Energy constraints of energy storage system:

[0258]

[0259] in, is the charging power of node i at time t, is the discharge power of node i at time t, P i gt is the peak power.

[0260] In some embodiments of the present invention, the robust feasible region of the objective function in the selected time period is used as an additional constraint, and an optimization algorithm is used to solve the optimal scheduling strategy in each selected time period, including:

[0261] Taking the robust feasible region of the objective function in the selected time period as an additional constraint, the minimization problem of the objective function is transformed into an optimal solution problem;

[0262] The raccoon optimization algorithm is used to solve the optimal solution problem and obtain the optimal scheduling strategy in each selected time period.

[0263] The above-mentioned multi-resource microgrid optimization scheduling device can execute the multi-resource microgrid optimization scheduling method provided by the aforementioned embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the multi-resource microgrid optimization scheduling method.

[0264] Figure 4 A schematic diagram of the structure of an electronic device provided by the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0265] like Figure 4As shown, the electronic device includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0266] A number of components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0267] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a multi-resource microgrid optimization scheduling method.

[0268] In some embodiments, the multi-resource microgrid optimization scheduling method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the multi-resource microgrid optimization scheduling method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the multi-resource microgrid optimization scheduling method in any other appropriate manner (e.g., by means of firmware).

[0269] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0270] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0271] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0272] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0273] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0274] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0275] An embodiment of the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the multi-resource microgrid optimization scheduling method provided in any embodiment of the present application.

[0276] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0277] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0278] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-resource microgrid optimization scheduling method, characterized in that: include: Taking the minimization of the total cost of the multi-resource microgrid as the goal, the objective function is constructed; Based on the constraints of the operation of the multi-resource microgrid, solving the robust feasible region of the objective function in each selected time period; Taking the robust feasible region of the objective function in the selected time period as an additional constraint, an optimization algorithm is used to solve the optimal scheduling strategy in each selected time period; The optimal dispatching strategies of each selected time period are integrated to obtain a global optimal solution, thereby achieving optimal allocation of the multi-resource microgrid.

2. The multi-resource microgrid optimization scheduling method according to claim 1 is characterized in that: The objective function is: min G=M grid +M turbine +M solar +M hydro +M storage +M punish Among them, M grid , M turbine , M solar , M hydro , M storage , M punish They represent the power procurement cost of the power grid, the consumption cost of the gas turbine, the maintenance cost of photovoltaic power generation, the maintenance cost of hydropower generation, the operation cost of the energy storage system, and the penalty cost caused by environmental factors; The electricity procurement cost is as follows: Where m buy,t is the electricity price of the relaxed node at time t, P 0,t represents the active power flowing into the node from the power grid, Δt is the time period of one hour, and T is the entire time period; The consumption cost of gas turbine is as follows: Where x, y, z are cost coefficients, P GST,k,t is the power generation of the kth gas turbine at time t, W is the total number of gas turbines; The maintenance cost of photovoltaic power generation is as follows: Among them, ξ PV is the unit operation and maintenance cost of the photovoltaic power station, is the photovoltaic power output of the kth photovoltaic power station at time t, C pv is the total number of photovoltaic power stations; The maintenance cost of hydropower generation is as follows: Among them, m M,k represents the operation and maintenance cost per unit power of k equipment, represents the output power of device k; The operating cost of the energy storage system is as follows: Among them, m storage is the energy storage cost coefficient, is the input power of the k energy storage system at time t, is the charging power coefficient, is the output power of the k energy storage system at time t, is the output power coefficient; The penalty cost is as follows: Among them, m punish,f is the penalty cost of wind curtailment, m punish,y is the penalty deviation coefficient, is the amount of wind power abandoned by wind turbine k at time t, U l,t is the voltage value of node l at time t, U refer is the node voltage reference value.

3. The multi-resource microgrid optimization scheduling method according to claim 2 is characterized in that: Solving the robust feasible region of the objective function in each selected time period includes: The objective function is rewritten in a compact form as follows: in, is the heat storage at time t, m t is the decision coefficient, h t is an equivalent variable, b t are independent decision variables, c t is the time constant, is the uncertain variable of new energy power output, M, N, K, L are weight coefficients; Right t The constructed uncertainty set is as follows: D t ={d t |0≤d t ≤d max } Among them, d max The maximum power output of wind power generation; The relevance of decision variables is defined as follows: The robust feasible region of each selected time period is defined as G t ,but G t =[G1,G2,…,G t-1 ] Among them, A m is the upper bound of the state function, A n is the lower bound of the state function, is the upper bound of energy storage output, is the lower bound of energy storage output, P GTS,min is the minimum output power of the gas turbine, P GTS,max is the maximum output power of the gas turbine, is the upper limit of photovoltaic power output, is the lower bound of photovoltaic power output.

4. The multi-resource microgrid optimization scheduling method according to claim 2 is characterized in that: It also includes: using binary component linearization to rewrite the gas turbine consumption cost function as: in, is the gas turbine cost coefficient, is the output power of the gas turbine at node k at time t.

5. The multi-resource microgrid optimization scheduling method according to claim 2 is characterized in that: Also included: Using binary component linearization to rewrite the penalty cost function as: Among them, λ l,t is the voltage change.

6. The multi-resource microgrid optimization scheduling method according to any one of claims 1 to 5, characterized in that: The constraints for the operation of the multi-resource microgrid include: (1) Active and reactive power balance constraints: Among them, P k,t is the active power of line k at time t, P l,t is the active power of line l at time t, P p,t is the load active power; Q k,t is the reactive power of line k at time t, Q l,t is the reactive power of line l at time t, Q p,t is the reactive power of the load; (2) Voltage amplitude constraint: in, is the voltage of node j of line k, is the voltage of node j in line l, U j,t is the voltage of node j at time t; (3) Gas turbine operation constraints: in, is the minimum output power of the gas turbine at node k, P GST,k,t is the output power of node k at time t, is the maximum output power of the gas turbine at node k; (4) PV power station output power constraints: in, is the minimum output power of the photovoltaic power station, is the maximum output power of the photovoltaic power station; (5) Hydropower station operation constraints: in, is the minimum output power of the hydropower station, is the maximum output power of the hydropower station; (6) Energy constraints of energy storage system: in, is the charging power of node i at time t, is the discharge power of node i at time t, P i gt is the peak power.

7. The multi-resource microgrid optimization scheduling method according to any one of claims 1 to 5, characterized in that: Taking the robust feasible region of the objective function in the selected time period as an additional constraint, an optimization algorithm is used to solve the optimal scheduling strategy in each selected time period, including: Taking the robust feasible region of the objective function in the selected time period as an additional constraint, the minimization problem of the objective function is transformed into an optimal solution problem; The raccoon optimization algorithm is used to solve the optimal solution problem and obtain the optimal scheduling strategy in each selected time period.

8. A multi-resource microgrid optimization dispatching device, characterized in that: include: An objective function building module is used to build an objective function with the goal of minimizing the total cost of the multi-resource microgrid; A feasible domain solving module, used for solving the robust feasible domain of the objective function in each selected time period based on the constraints of the operation of the multi-resource microgrid; An optimal scheduling strategy solving module is used to solve the optimal scheduling strategy in each selected time period using an optimization algorithm with the robust feasible region of the objective function in the selected time period as an additional constraint; The global optimal solution integration module is used to integrate the optimal dispatching strategies of each selected time period to obtain the global optimal solution and realize the optimal allocation of the multi-resource microgrid.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the multi-resource microgrid optimization scheduling method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the multi-resource microgrid optimization scheduling method as described in any one of claims 1 to 7 is implemented.

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