Virtual power plant real-time day-ahead optimization scheduling method based on improved Harris eagle optimization

Abstract

The invention discloses a virtual power plant real-time day-ahead optimization scheduling method based on improved Harlisia eagle optimization, and the method comprises the steps: constructing an improved virtual power plant scheduling model, and optimizing a target function of the virtual power plant scheduling model through employing a risk measurement technology condition risk value CVaR; modeling scheduling resources of the virtual power plant, wherein the scheduling resources comprise a wind power plant, a solar photovoltaic power station, a cogeneration unit and a fuel cell; an electric vehicle and an energy storage system are incorporated into the virtual power plant, and power balance in the virtual power plant is achieved; and optimizing the virtual power plant scheduling model by adopting an improved Harris eagle optimization algorithm to realize real-time day-ahead optimal scheduling of the virtual power plant. According to the invention, flexible standby is provided by the electric vehicle, and spinning standby is provided by the energy storage system, so that the reliability and safety of virtual power plant operation are improved; and solving the real-time scheduling model of the virtual power plant by applying an improved Harris Hawk optimization algorithm MHHO to realize day-ahead scheduling and real-time scheduling, so that the minimum emission value is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of electric power dispatching, and in particular relates to a real-time day-ahead optimization dispatching method for a virtual power plant based on improved Harris Hawk optimization. Background Art

[0002] The further penetration of distributed energy resources (DERs) in the grid system will bring new challenges to utility grid operators due to their inherent intermittency, lack of visibility, minimal controllability provided, and problems related to power quality. At the same time, the presence of distributed grids in the main grid also provides a lot of flexibility that can be used to advance and improve network-related operations. Since the power system is in the stage of transition from a centralized conventional structure to a decentralized system equipped with unconventional power sources, it needs to be properly controlled and managed. Virtual power plants (VPPs) highlight the untapped potential of DERs and make them available to utility operators who can further use them in the deregulated energy market and also for ancillary services. VPPs can provide multifunctional services without investing any additional costs for setting up power plants. These high-tech advances enable operators to join or abandon any resource at the right time according to the supply and demand of the system. In the context of sustainable living, the two most important aspects are energy security and climate change, considering that this will ensure that the concept of power for all becomes a reality. To generate clean energy, solar photovoltaic and wind energy are the main resources for promoting renewable energy technologies.

[0003] The energy storage aspect is also in line with it accordingly, that is, it is mainly achieved through energy storage technologies (ESTs) in the form of batteries, rather than mobile electric vehicles (EVs). The deployment of renewable energy sources from different locations, if not properly controlled and handled, will definitely increase challenges on the distribution side and make full use of the additional benefits it provides, namely increased system flexibility and various other grid-related services. In the context of DERs visibility, VPP plays a very important role in network operations. In short, the VPP system undoubtedly encourages prosumers to contribute more to environmental protection and create a more sustainable and greener future for the energy system sector by promoting a green economy. Summary of the invention

[0004] Purpose of the invention: The present invention proposes a real-time day-ahead optimization scheduling method for a virtual power plant based on improved Harris Hawk optimization, which improves the reliability and safety of the system and reduces the minimum emission value.

[0005] Technical solution: The real-time day-ahead optimization scheduling method for a virtual power plant based on improved Harris Hawk optimization described in the present invention specifically includes the following steps:

[0006] (1) Build an improved virtual power plant dispatch model and use the risk metric CVaR to optimize the objective function of the virtual power plant dispatch model;

[0007] (2) Modeling the dispatch resources of virtual power plants, including wind power plants, solar photovoltaic power plants, combined heat and power units, and fuel cells;

[0008] (3) Incorporate electric vehicles and energy storage systems into virtual power plants to achieve power balancing in virtual power plants;

[0009] (4) The improved Harris Hawk optimization algorithm (MHHO) is used to optimize the virtual power plant scheduling model to achieve real-time day-ahead optimal scheduling of the virtual power plant.

[0010] Furthermore, the implementation process of step (1) is as follows:

[0011] Net profit is considered as the main objective function to be maximized, and the constraints are handled using dynamic penalties:

[0012]

[0013] Among them, s and p are the sets of scenarios and power stations, t represents the day-ahead dispatch time, and c ph and c se is the price of electricity purchased from the grid, π s is the probability of the scenario p em is the price related to the energy market, c ens is the price of unserved energy, c chp and c hou is the cost function for heat and thermal power generation only, p hou is the price per unit of heat;

[0014] The parameter β is the risk level, β = 0 indicates risk neutrality, and β = 1 indicates risk aversion decision; the higher the β value, the lower the profit value; when β = 0, the objective function is as shown in formula (1), when β = 0.5, the objective function is updated to formula (2) and modified according to the proposed formula; for the desired problem statement, the risk management model considered is evaluated with three different β values, and it is emphasized to give equal weight throughout the study:

[0015] Totalprofit=(1-β)NetProfit+(β)CVaR (2)

[0016]

[0017] Among them, β is the risk level, CVaR is the conditional value at risk, η is the CVaR variable, and δ represents the distance between the bad value of the scenario and the CVaR variable;

[0018] The secondary objective function is to reduce the emissions of the system:

[0019]

[0020] Among them, e chp and e hou Emissions from CHP and HOU; ph , e se is the emission of the main network;

[0021] The total emissions from the main grid for the combined heat and power (CHP) unit alone are:

[0022]

[0023]

[0024]

[0025] Among them, P ph+se is the power exchanged from the upstream network;

[0026] Treat both objectives with equal weight, i.e. profit increase followed by emission minimization:

[0027] Fitness=w×Totalprofit+(1-w)×Emission (8).

[0028] Furthermore, the wind farm in step (2) is:

[0029]

[0030] Among them, P wind is the output power, P rated is the rated power, is the wind speed, V b , V s are the cut-in and cut-out wind speeds, V e is the rated wind speed.

[0031] Furthermore, the solar photovoltaic power station in step (2) is:

[0032]

[0033] V s,t =V oc -K v ×T Cs,t (11)

[0034]

[0035] in, is the output power of solar photovoltaic power generation, is the number of photovoltaic modules, sor s,t is solar radiation, V oc and I SC is the open circuit voltage and short circuit current, K i is the current temperature, K v is the voltage temperature coefficient, T Cs,t is the solar cell temperature.

[0036] Furthermore, the cogeneration unit in step (2) is:

[0037] Considering two CHP units with different feasible operating areas, the maximum electrical and thermal output power is contracted as:

[0038]

[0039]

[0040] in, is the output power of the combined heat and power generation, It is the status of the cogeneration unit.

[0041] Furthermore, the fuel cell in step (2) is:

[0042] The efficiency of a fuel cell is related to the partial load ratio; the mathematical expression related to the PLR ​​function is:

[0043] When PLR<0.05:

[0044]

[0045] When PLR ≥ 0.05:

[0046]

[0047]

[0048] in, For FC in i th Efficiency (pu), For FC in i th The thermal-to-electric ratio of the fuel cell is given by The difference between the power extracted by the cell and the power produced in the previous interval is limited to a certain value:

[0049]

[0050] Where ΔP ft_up. is climbing uphill, ΔP ft_dowm. It's downhill. Is FCs in i thThe power generated when is the power produced by FCs over the previous interval.

[0051] Furthermore, the energy storage system in step (3) is:

[0052]

[0053] in, represents the cost of ESS degradation, is the charge or discharge of ESSs, and the charge state of ESSs is:

[0054]

[0055] Equations (21) to (23) represent the limits according to the aging degree of ESSs:

[0056]

[0057]

[0058]

[0059] In the formula, and The minimum and maximum ESSs stored energy, and are the minimum and maximum energy storage factors, represents the capacity of ESS, and the charge and discharge rate constraints of ESSs are:

[0060]

[0061]

[0062] In the formula, and Indicates the ESS charging and discharging efficiency; and is the maximum charge and discharge rate of ESS;

[0063] For any given time interval, the charge or discharge state is one of:

[0064]

[0065] The constraints related to the spinning reserve component are defined in (27) to (29), supporting the VPP as a spinning reserve:

[0066]

[0067]

[0068]

[0069] Where P max and SRS ESS is the maximum power limit and spinning reserve of the ESS form, RR ESS and SRS req It is used for ramping and spinning reserve requirements of ESS.

[0070] Furthermore, the electric vehicle in step (3) is used as a flexible reserve, which is:

[0071]

[0072]

[0073] in, and Indicates the charging and discharging status of electric vehicles. For electricity price, Cost of electric vehicles based on driving distance;

[0074] Limitations to consider to avoid premature aging are:

[0075]

[0076]

[0077]

[0078] in, and is the minimum and maximum energy stored in the EV, and are the minimum and maximum EV energy storage coefficients, is the electric vehicle battery capacity;

[0079] The state of charge of the electric vehicle charge and discharge rate is estimated as:

[0080]

[0081]

[0082] In the formula, and Indicates the charging and discharging efficiency of electric vehicles, and is the charge and discharge capacity of the electric vehicle;

[0083] According to the above restrictions, electric vehicles can only use one SoC, SoD and driving mode:

[0084]

[0085] In the formula, Only a specific charging, discharging or driving state is valid for electric vehicles;

[0086] When an EV is not in a parking spot, the energy stored in the EV battery is consumed through battery discharge:

[0087]

[0088] At any given moment, the energy available to cover this distance is given by (39), followed by the energy required for daily travel in (40):

[0089]

[0090]

[0091] Where ECNF is the energy conversion factor, Indicates the electrical load and the driving distance of the electric vehicle;

[0092] In (41) to (43) the constraints related to flexible reserve are defined, based on which EVs can support VPP as flexible reserve:

[0093]

[0094]

[0095]

[0096] Where, FRS EV It is a flexible backup for electric vehicles. EV and FRS req It is the hill climbing and flexible reserve requirement for electric vehicles.

[0097] Furthermore, the power balance in the virtual power plant in step (3) is:

[0098]

[0099] In the formula, For equivalent output power, For power exchange between the grid and the VPP, is the efficiency of the fuel cell, is the electricity generated by wind turbines, It is solar photovoltaic power generation. is the available power of the EV as a flexible reserve, is the available power from the ESS as spinning reserve, For power load demand.

[0100] Furthermore, the improved Harris Hawk optimization algorithm in step (4) is:

[0101] To make the search space more diverse, the eagle step size is increased, r1 and r3 are selected from four random numbers, and they are modeled using an exploration factor, which is:

[0102]

[0103] According to the exploration coefficients, due to some changes in r1 and r3, the vector position of the search body in subsequent iterations is updated to:

[0104]

[0105] A chaotic sequence is used to make the distribution of random numbers more uniform; this mapping is suitable for enhancing the initial position of the search agent, which is expressed as:

[0106]

[0107] In the formula, X i+1 represents the position of the new eagle after mapping, a is the parameter set by the algorithm test, and the random walk strategy is selected to improve the local optimal trap and sort the same fitness value as the iteration proceeds, and continue to move forward; activate a variable parameter to establish synchronization with the position of the search agent, which is expressed as:

[0108]

[0109] In the formula, X n (i) is the new position after applying the random walk. By retaining the solution with better performance, the next subsequent iteration will be performed according to formula (63):

[0110]

[0111] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention uses electric vehicles to provide flexible backup and the energy storage system to provide rotating backup to improve the reliability and safety of the virtual power plant operation; the improved Harris Hawk optimization algorithm MHHO is used to solve the real-time scheduling model of the virtual power plant, realize day-ahead scheduling and real-time scheduling, and reduce the minimum emission value. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 is the VPP system configuration under study;

[0113] Figure 2 The feasible operation areas of CHP are (a) Type I, (b) Type II;

[0114] Figure 3 It is a flowchart of the improved HHO algorithm. DETAILED DESCRIPTION

[0115] The present invention is further described in detail below based on the accompanying drawings.

[0116] The present invention proposes a virtual power plant real-time day-ahead optimization scheduling method based on improved Harris Hawk optimization, which uses improved Harris Hawk optimization algorithm (MHHO) to optimize the scheduling of virtual power plants (VPPs) and considers conditional risk (CVaR) to improve the economy and environmental performance of renewable energy networks. Specifically, the method comprises the following steps:

[0117] Step 1: Construct an improved virtual power plant scheduling model and use the risk measurement technical condition risk value CVaR to optimize the objective function of the virtual power plant scheduling model.

[0118] The detailed description of the VPP system is as follows Figure 1 As shown, wind, solar PV and FC are considered renewable energy sources, followed by combined heat and power systems working as CHP units, which are also included in the resources due to their higher efficiency. The energy market and electricity prices are indeed an integral part of the VPP and are also taken into account. A storage configuration is also provided, in which EVs are designated as flexible reserve while ESS is selected to provide spinning reserve in the system. VPP purchases energy from the wholesale market to maintain the energy balance gap, and the surplus electricity is sold in the distributed power market. In the current configuration, EVs and ESSs are specifically included because EVs are considered as a soft load that can be utilized in the form of mobile storage systems, followed by ESSs, which can increase the overall flexibility of the system.

[0119] The intended objective functions are multiple, i.e. the primary objective function deals with net profit growth and the secondary objective function focuses on emission reduction.

[0120] Net profit is considered as the main objective function to be maximized, and a dynamic penalty is used in the present invention which is superior to the static penalty:

[0121]

[0122] Where: s and p are the sets of scenarios and power stations, t represents the day-ahead dispatch time (1-24 hours), c ph and c se is the price of electricity purchased from the grid, π s is the probability of the scenario, p em is the price related to the energy market, c ens is the price of unserved energy, c chp and c hou is the cost function for heat and heat and power units (HOUs) only, phou is the price per unit of heat.

[0123] The electricity prices in the retail and wholesale markets have a certain volatility, which is an uncertain factor in the electricity trading market. This uncertainty affects the operation plan of the VPP, making the whole process very challenging for the VPP operator, and ultimately leads to risks. In order to keep it within the prescribed limits, risk management is used, in which the very famous measurement conditional risk value (CVaR) is included based on the probability of the scenario without involving additional discrete variables. The parameter β is considered as the risk level, β = 0 indicates risk neutrality, and β = 1 indicates risk averse decision. The higher the β value, the lower the profit value. When β = 0, the objective function is similar to (1), and when β = 0.5, the objective function is updated to (2) and modified according to the proposed formula. The risk management model considered for the desired problem statement is evaluated with three different β values, emphasizing that the same weight is given:

[0124] Totalprofit=(1-β)NetProfit+(β)CVaR (2)

[0125]

[0126] Where: β is the risk level, CVaR is the conditional value at risk, η is the CVaR variable, and δ represents the distance between the scenario bad value and the CVaR variable.

[0127] The secondary objective function represented by equation (4) specifically deals with reducing emissions from the system because one of the resources includes a cogeneration unit.

[0128]

[0129] Where: e chp and e hou Emissions from CHP and HOU. ph , e se is the emission of the main network.

[0130] The total emissions from the main grid generated by only the thermal unit of cogeneration can be estimated by formula (5-7). The unit of emission factor is (Kg / MWh).

[0131]

[0132]

[0133]

[0134] Among them, P ph+se is the power exchanged from the upstream network.

[0135] In equation (7), the emissions associated with the main grid are measured only when electricity is obtained from the electricity market. Considering only one direction of electricity purchase and sale, the emissions generated by the grid are only relevant when electricity is purchased from the market. In the present work, the electricity emissions of the grid are properly considered, and the credits for reducing the emissions of alternative generation by customers are in line with social welfare, which will open up a different research avenue and will be extended in future work.

[0136] In order to obtain the best possible compromise between the two objectives, the problem formulation of the VPP is carried out using a suitable method of weighting factors as shown in (8). In order to treat the two objectives with equal weights, i.e. profit increase followed by emission minimization, the VPP operator can choose the best possible compromise.

[0137] Fitness×w×Totalprofit+(1-w)×Emission (8)

[0138] Step 2: Model the dispatching resources of the virtual power plant, including wind power plants, solar photovoltaic power plants, cogeneration units, and fuel cells.

[0139] Wind energy is the most widely used renewable resource in the world and has great potential. The output power extracted by a wind turbine is calculated using equation (9).

[0140]

[0141] Where: P wind is the output power, P rated is the rated power, is the wind speed, V b , V s are the cut-in and cut-out wind speeds, V e is the rated wind speed.

[0142] The output of solar photovoltaic modules is largely affected by certain parameters, namely solar irradiance, typical characteristics of solar panels, and ambient temperature at a specific location. Sunny day solar energy availability is abundant and generally obeys the beta probability distribution function (PDF). The features of this module are as follows:

[0143]

[0144] V s,t =V oc -K v ×T Cs,t (11)

[0145]

[0146] in: is the output power of solar photovoltaic power generation, is the number of photovoltaic modules, sor s,t is solar radiation (KW / m2), V oc and I SC is the open circuit voltage and short circuit current, K i is the current temperature, K v is the voltage temperature coefficient, T Cs,t is the solar cell temperature.

[0147] In order to achieve the optimal dispatch, the present invention considers two types of CHP units with different feasible operating regions (FOR). The maximum electrical and thermal output power contraction is described as:

[0148]

[0149]

[0150] in: is the output power of the combined heat and power generation, is the state of the combined heat and power unit. The FOR of the type 1 combined heat and power unit is convex, and the FOR of the type 2 is non-convex, such as Figure 2 shown.

[0151] The efficiency of a fuel cell (FC) is related to the partial load ratio (PLR). The mathematical expression related to the PLR ​​function is:

[0152] When PLR<0.05:

[0153]

[0154] When PLR ≥ 0.05:

[0155]

[0156]

[0157] in: For FC in i th Efficiency (pu), For FC in i th The thermal-to-electric ratio (MW). The ramp limit of the fuel cell is given by The difference between the power extracted by the cell and the power produced in the previous interval is limited to a certain value:

[0158]

[0159] Where: ΔP ft_up. is climbing uphill, ΔP ft_dowm. It's downhill climbing. Is FCs in ith The power generated when is the power produced by FCs over the previous interval.

[0160] Step 3: Incorporate electric vehicles and energy storage systems into the virtual power plant to achieve power balancing in the virtual power plant.

[0161] In the current problem statement, storage facilities are provided in the form of electric vehicles, which are considered as soft loads, as flexible reserves, while ESS is suitable for the main reserve options. In order to balance the gap between supply and demand, these storage options are effective when the electricity from renewable energy sources is not enough to meet the electricity demand. This will also allow electric vehicle owners to participate in the VPP system and receive some rewards, becoming active prosumers and helping to relieve congestion in the utility grid network.

[0162] Incorporating ESS into virtual power plants helps achieve optimal management of renewable resources, effectively control and coordinate renewable resources, and maintain uninterrupted balance of supply and demand in the grid. Another essential feature of ESS in the system model is their ability to overcome grid instability problems, namely voltage imbalance, frequency regulation, which may occur due to the unpredictable and volatile nature of renewable energy sources (RERs), which may cause rapid voltage fluctuations and sharp deviations in system frequency. The mathematical expression used to model the system in its current configuration is expressed as:

[0163]

[0164] in: represents the cost of ESS degradation, is the charge / discharge of ESSs, and the state of charge (SoC) of ESSs is evaluated according to (20):

[0165]

[0166] The limits of ESSs aging degree are:

[0167]

[0168]

[0169]

[0170] Where: and The minimum and maximum ESSs stored energy, and are the minimum and maximum energy storage factors, represents the capacity of ESS. The charge and discharge rate constraints of ESSs are:

[0171]

[0172]

[0173] Where: and represents the ESS charging and discharging efficiency, and is the maximum charge and discharge rate of ESS.

[0174] For any given time interval, the charge or discharge state is one of:

[0175]

[0176] The constraints associated with the spinning reserve component are defined in (27-29), based on which the ESS is able to support the VPP as a spinning reserve.

[0177]

[0178]

[0179]

[0180] Where: P max and SRS ESS The maximum power limit and spinning reserve of the ESS. ESS and SRS req It is used for ramping and spinning reserve requirements of ESS.

[0181] Electric vehicles are considered a flexible energy source that can serve as a mobile storage option and can relieve congestion in the grid by running them. Most of the time, electric vehicles are not fully utilized, either in parking lots or in residential areas. The respective EV owners can be benefited by associating them with a virtual power plant, which enables them to receive some rewards by participating in prosumer services and providing storage systems during peak load periods. It is important to mention that there is a predetermined range of SoC associated with each associated EV. In the current VPP system, EVs are used as flexible reserves:

[0182]

[0183]

[0184] in: and Indicates the charging and discharging status of electric vehicles. For electricity price, The cost of an electric vehicle based on driving distance.

[0185] Limitations to consider to avoid premature aging:

[0186]

[0187]

[0188]

[0189] in: and It is the minimum maximum energy stored in the EV. and are the minimum and maximum EV energy storage coefficients. is the battery capacity of an electric vehicle.

[0190] The state of charge (SoC) of an electric vehicle’s charge and discharge rate is estimated as:

[0191]

[0192]

[0193] Where: and Indicates the charging and discharging efficiency of electric vehicles. and It is the charge and discharge capacity of the electric vehicle.

[0194] According to the above restrictions, electric vehicles can only use one SoC, SoD and driving mode:

[0195]

[0196] Where: Only a specific charging, discharging or driving state is valid for electric vehicles.

[0197] When an EV is not in a parking spot, the energy stored in the EV battery is consumed through battery discharge:

[0198]

[0199] At any given moment, the energy available to cover this distance is given by (39), followed by the energy required for daily travel in (40):

[0200]

[0201]

[0202] Where: ECNF is the energy conversion coefficient. It represents the power load and the driving distance of electric vehicles in kms.

[0203] The constraints related to flexible standby are:

[0204]

[0205]

[0206]

[0207] Where: FRS EV It is a flexible backup for electric vehicles. EV and FRS req are the ramping and flexible reserve requirements for EVs; based on these constraints EVs can support VPP as flexible reserves.

[0208] Combining various resources into a flexible virtual power plant plays an important role in maintaining the stability of overall power generation and consumption and achieving power balance. It is expressed as:

[0209]

[0210] Where: For power exchange between the grid and the VPP, is the efficiency of the fuel cell, is the electricity generated by wind turbines, It is solar photovoltaic power generation. is the available power of the EV as a flexible reserve, is the available power from the ESS as spinning reserve, For power load demand.

[0211] Step 4: Figure 3 As shown in the figure, the improved Harris Eagle optimization algorithm is used to optimize the virtual power plant scheduling model to achieve real-time day-ahead optimal scheduling of the virtual power plant.

[0212] Exploration Phase: In this phase, exploration is performed and Harris hawk is assumed as the search agent and the prey (i.e. rabbit) is predicted as the best candidate solution. An appropriate number of hawks are placed at different locations to locate the prey based on their innovative strategies. This concept is mathematically defined as:

[0213]

[0214] Where: X(t+1) represents the vector position of the search subject in the subsequent iteration, X(t) represents the current position vector of the search subject, and X rabbit (t) represents the position of the rabbit at the tth iteration, X rand(t) represents the position of the random eagle, X m (t0 represents t th The average position of the existing eagle population at the iteration, UB and LB are the upper and lower limits, q and r 1 、r 2 、r 3 、r 4 A random number between (0 and 1).

[0215]

[0216] Where: HWK is the number of eagles in the population, X i (t) is the number of times each eagle th The position at the iteration.

[0217] Switching from exploration to exploitation: After the exploration phase, the algorithm switches from exploration behavior to exploitation behavior based on the rabbit's escape energy. It is worth noting that with the increase in the number of iterations, the energy of the prey decreases significantly due to the rabbit's escape behavior. In this case, the eagle explores different areas to determine the location of the rabbit. When |E|≥1, the algorithm is in the exploration phase. In the next phase, when |E|<1, the algorithm starts to execute the exploitation phase. The energy of the prey is expressed as:

[0218]

[0219] Where: E is the rabbit's escape energy, E0 is the rabbit's initial energy state, and T is the maximum number of iterations.

[0220] Exploitation Phase: Once the transformation phase is completed, the algorithm moves to the next phase, which is the most critical step as the eagle chases the prey and eventually captures it. Four pursuit styles are discussed in this paper due to the escape behavior of the rabbit. The energy of the rabbit is represented by (E). When |E|≥0.5, soft entrapment occurs and when |E|<0.5, hard entrapment occurs. In any threatening situation, the victim will always try to escape, which can be assumed to be a random number between 0 and 1. It is represented by r, which is the chance of the rabbit to escape safely, i.e. r<0.5 means that the rabbit successfully escapes, while r≥0.5 means that the rabbit failed in its attempt before the "surprise attack".

[0221] Soft siege: This method is applicable when r ≥ 0.5 and |E| ≥ 0.5. The eagle gently surrounds the prey, causing it to escape, and eventually makes the prey weak and exhausted, as shown in (48).

[0222] X(t+1)=ΔX(t)-E|JX rabbit (t)-X(t)| (48)

[0223] Where: ΔX(t) is the change in the position vectors of the rabbit and the eagle, and the J value changes rapidly in each subsequent iteration.

[0224] ΔX(t)=X rabbit (t)-X(t) (49)

[0225] Hard siege: When r ≥ 0.5 and |E| < 0.5, the target energy becomes slightly smaller. Seizing this opportunity, the eagle surrounds the rabbit and performs a “surprise attack” as described in (50).

[0226] X(t+1)=X rabbit (t)-E|ΔX(t)| (50)

[0227] Soft siege with rapid advance (SBPRD): This stage is a bit smarter than the previous actions. In this strategy, when |E|≥0.5 and r<0.5, the rabbit still has remaining energy and builds a soft siege. The eagle chooses the next action according to (51), which applies to all search agents.

[0228] Y=X rabbit (t)-E|JX rabbit (t)-X(t)| (51)

[0229] Z=Y+S×LF(D) (52)

[0230] Where: Y is the suitable new position of the eagle, LF is the fighting function, D is the number of variables, S is the random vector; the fighting function is:

[0231]

[0232] Where: β is a constant value equal to 1.5, u and v are random values ​​in the range of (0,1).

[0233]

[0234] According to (55), the final update strategy of the eagle's position in this stage is shown in (55).

[0235]

[0236] Hard encirclement with rapid advance (HBPRD): In the last step, no matter |E| < 0.5 or r < 0.5, the prey is exhausted and has no energy to escape. At this stage, the only additional action of the hawks is that they try to reduce the average distance from the prey position:

[0237]

[0238] Y and Z are:

[0239] Y=X rabbit (t)-E|JX rabbit (t)-X m (t)| (57)

[0240] Z=Y+S×LF(D) (58)

[0241] In addition to the stages and steps discussed so far, some parameters were adjusted to make the algorithm more flexible so that the rabbit can be easily located. The following modifications were made in both exploration and exploitation. In order to make the search space more diverse, the eagle's step size must be increased, r1 and r3 are selected from four random numbers, and they are modeled using the exploration factor, which is:

[0242]

[0243] According to the exploration coefficient, due to some changes in r1 and r3, (45) will now be updated and revised to (60):

[0244] Chaotic mapping is adapted and applied to the initialization of the population or the early stages of exploration. In order to make the algorithm more stable, a chaotic sequence is used to make the distribution of random numbers more uniform. This mapping is suitable for enhancing the initial position of the search agent, which is expressed as:

[0245]

[0246] Where: X i+1 It represents the position of the new eagle after mapping, a is set to 0.7, and the random walk strategy is selected to improve the local optimal trap and sort the same fitness value as the iteration proceeds. A variable parameter is activated to establish synchronization with the position of the search agent, which is expressed as:

[0247]

[0248] Where: X n (i) is the new position after applying the random walk, and c is set to 6. By retaining the solution with better performance, the next subsequent iteration will be performed according to (63):

[0249]

[0250] The resources included in the VPP system proposed in this invention are solar photovoltaic, wind energy and fuel cells, which are all classified as renewable energy sources (RERs), in addition to a combined heat and power (CHP) unit, which contributes to the overall efficiency of the system, but at the same time releases emissions. In addition to these units, the VPP is also associated with operational storage to improve the overall reliability of the system, namely electric vehicles (EV) and energy storage systems (ESS). It is worth noting that in terms of thermal and electrical energy efficiency, CHP units are suitable, but at the same time ESS undoubtedly has advantages from an environmental point of view.

[0251] In the MATLAB environment, version R2019a, random programming was performed on a laptop with an Intel Core i5 CPU and 8GB RAM, running Windows 11 (64-bit) operating system with a clock speed of 2.90GHz. For the two objective functions, the selected metaheuristic technique was run for 200 iterations with a population size of 30, followed by 100 independent trials to obtain a high-quality solution set, and then the numerical results for each parameter are listed. The algorithm proposed in this patent is compared with the conventional solution algorithm, as shown in Table 1, where MOPSO is multi-objective particle swarm optimization, MILP is mixed integer linear programming, and MHHO is improved Harris Hawk optimization.

[0252] Three cases are clearly presented in the discussion, where total profit maximization is performed in case 1, emission minimization is performed in case 2, and dual objective scheduling is discussed in case 3; where total profit maximization and emission minimization are achieved simultaneously. The reason for the reduction in profit values ​​obtained in MILP is that they are known as exact optimization methods, where it is guaranteed to find the optimal solution, but they are not suitable for handling complex types of problems. The main disadvantage is that their solution time increases exponentially compared to the proposed Harris Eagle Optimization Algorithm, which is a recently developed nature-inspired technique and is considered to be a meta-heuristic technique that can handle nonlinear problems more effectively in terms of accuracy and robustness. Moreover, they are sensitive to parameters, and in order to balance the exploration and exploitation capabilities, the adjustment of parameters plays an important role in reaching the optimal solution. In summary, the solution algorithms proposed in the present invention are stochastic and nature-inspired, and they perform better due to their flexible structure, low computational cost, reduced average running time, and gradient-free mechanism.

[0253] Case 1: In Table 1, the numerical results for various profit parameters are listed and compared with the published work. It can be observed that the optimal solution obtained by the proposed solution algorithm for day-ahead scheduling is improved by 24.38% and 46.84% respectively compared with the conventional solution algorithms MOPSO and MILP. For real-time scheduling, the optimal solution of total profit is improved by an additional 0.48% compared with day-ahead scheduling.

[0254] Table 1 Comparison of Profit Maximization Case 1

[0255]

[0256] Case 2: Table 2 lists the numerical results of each parameter, namely the minimum, maximum and average emissions, as well as the average calculation time required to reach the optimal value. Case 2 is emission minimization, and compared with the solution results of the conventional algorithm, it is reduced by 19.1% and 7.4%, respectively. In addition, with real-time scheduling, the emission value is further reduced by 0.4%. The minimum pollution emission value can be obtained with a smaller number of iterations, and it is obvious that the optimal solution result obtained by the solution algorithm (MHHO method) proposed in the present invention is significantly better.

[0257] Table 2 Comparison of emission reductions in Case 1

[0258]

[0259] Case 3: The payoff table in Table 3 is specific to Case 3, which is a dual-objective case obtained by running the program after repeated trials. In Case 3, scheduling was performed simultaneously, specifically mentioning the values ​​of achieving maximum profit and minimum emission simultaneously. On the other hand, Tables 1 and 2 represent the values ​​of profit and emission obtained separately in Case 1 and Case 2, respectively. The solution obtained by dual-objective scheduling shows significant improvement, and the performance of the proposed algorithm is significantly improved compared to the conventional algorithm.

[0260] Table 3 Comparison of dual-objective optimization effects in case 3

[0261]

[0262] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A virtual power plant real-time day-ahead optimization scheduling method based on improved Harris Hawk optimization, characterized in that: The following steps are involved: (1) Build an improved virtual power plant dispatch model and use the risk metric CVaR to optimize the objective function of the virtual power plant dispatch model; (2) Modeling the dispatch resources of virtual power plants, including wind power plants, solar photovoltaic power plants, combined heat and power units, and fuel cells; (3) Incorporate electric vehicles and energy storage systems into virtual power plants to achieve power balancing in virtual power plants; (4) The improved Harris Hawk optimization algorithm (MHHO) is used to optimize the virtual power plant scheduling model to achieve real-time day-ahead optimal scheduling of the virtual power plant.

2. The virtual power plant real-time day-ahead optimization scheduling method based on improved Harris Hawk optimization according to claim 1 is characterized in that: The implementation process of step (1) is as follows: Net profit is considered as the main objective function to be maximized, and the constraints are handled using dynamic penalties: Among them, s and p are the sets of scenarios and power stations, t represents the day-ahead dispatch time, and c ph and c se is the price of electricity purchased from the grid, π s is the probability of the scenario p em is the price related to the energy market, c ens is the price of unserved energy, c chp and c hou is the cost function for heat and thermal power generation only, p hou is the price per unit of heat; The parameter β is the risk level, β = 0 indicates risk neutrality, and β = 1 indicates risk aversion decision; the higher the β value, the lower the profit value; when β = 0, the objective function is as shown in formula (1), when β = 0.5, the objective function is updated to formula (2) and modified according to the proposed formula; for the desired problem statement, the risk management model considered is evaluated with three different β values, and it is emphasized to give equal weight throughout the study: Totalprofit=(1-β)NetProfit+(β)CVaR (2) Among them, β is the risk level, CVaR is the conditional value at risk, η is the CVaR variable, and δ represents the distance between the bad value of the scenario and the CVaR variable; The secondary objective function is to reduce the emissions of the system: Among them, e chp and e hou Emissions from CHP and HOU; ph , e se is the emission of the main network; The total emissions from the main grid for the combined heat and power (CHP) unit alone are: Among them, P ph+se is the power exchanged from the upstream network; Treat both objectives with equal weight, i.e. profit increase followed by emission minimization: Fitness=w×Totalprofit+(1-w)×Emission (8).

3. The virtual power plant real-time day-ahead optimization scheduling method based on improved Harris Hawk optimization according to claim 1 is characterized in that: The wind farm in step (2) is: Among them, P wind is the output power, P rated is the rated power, V t b is the wind speed, V b , V s are the cut-in and cut-out wind speeds, V e is the rated wind speed.

4. The virtual power plant real-time day-ahead optimization scheduling method based on improved Harris Hawk optimization according to claim 1 is characterized in that: The solar photovoltaic power station in step (2) is: V s,t =V oc -K v ×T Cs,t (11) in, is the output power of solar photovoltaic power generation, is the number of photovoltaic modules, sor s,t is solar radiation, V oc and I SC is the open circuit voltage and short circuit current, K i is the current temperature, K v is the voltage temperature coefficient, T Cs,t is the solar cell temperature.

5. The method for real-time day-ahead optimization scheduling of virtual power plants based on improved Harris Hawk optimization according to claim 1 is characterized in that: The cogeneration unit in step (2) is: Considering two CHP units with different feasible operating areas, the maximum electrical and thermal output power is contracted as: in, is the output power of the combined heat and power generation, It is the status of the cogeneration unit.

6. The method for real-time day-ahead optimization scheduling of virtual power plants based on improved Harris Hawk optimization according to claim 1 is characterized in that: The fuel cell in step (2) is: The efficiency of a fuel cell is related to the partial load ratio; the mathematical expression related to the PLR ​​function is: When PLR<0.05: When PLR ≥ 0.05: in, For FC in i th Efficiency (pu), For FC in i th The thermal-to-electric ratio of the fuel cell is given by The difference between the power extracted by the cell and the power produced in the previous interval is limited to a certain value: Among them, ΔP ft_up. is climbing uphill, ΔP ft_dowm. It's downhill. Is FCs in i th The power generated when is the power produced by FCs over the previous interval.

7. The method for real-time day-ahead optimization scheduling of virtual power plants based on improved Harris Hawk optimization according to claim 1 is characterized in that: The energy storage system in step (3) is: in, represents the cost of ESS degradation, is the charge or discharge of ESSs, and the charge state of ESSs is: Equations (21) to (23) represent the limits according to the aging degree of ESSs: In the formula, and The minimum and maximum ESSs stored energy, and are the minimum and maximum energy storage factors, represents the capacity of ESS, and the charge and discharge rate constraints of ESSs are: In the formula, and Indicates the ESS charging and discharging efficiency; and is the maximum charge and discharge rate of ESS; For any given time interval, the charge or discharge state is one of: The constraints related to the spinning reserve component are defined in (27) to (29), supporting the VPP as a spinning reserve: Where P max and SRS ESS is the maximum power limit and spinning reserve of the ESS form, RR ESS and SRS req It is used for ramping and spinning reserve requirements of ESS.

8. The virtual power plant real-time day-ahead optimization scheduling method based on improved Harris Hawk optimization according to claim 1 is characterized in that: The electric vehicle described in step (3) is used as a flexible reserve, which is: in, and Indicates the charging and discharging status of electric vehicles. For electricity price, Cost of electric vehicles based on driving distance; Limitations to consider to avoid premature aging are: in, and is the minimum and maximum energy stored in the EV, and are the minimum and maximum EV energy storage coefficients, is the electric vehicle battery capacity; The state of charge of the electric vehicle charge and discharge rate is estimated as: In the formula, and Indicates the charging and discharging efficiency of electric vehicles, and is the charge and discharge capacity of the electric vehicle; According to the above restrictions, electric vehicles can only use one SoC, SoD and driving mode: In the formula, Only a specific charging, discharging or driving state is valid for electric vehicles; When an EV is not in a parking spot, the energy stored in the EV battery is consumed through battery discharge: At any given moment, the energy available to cover this distance is given by (39), followed by the energy required for daily travel in (40): Where ECNF is the energy conversion factor, Indicates the electrical load and the driving distance of the electric vehicle; In (41) to (43) the constraints related to flexible reserve are defined, based on which EVs can support VPP as flexible reserve: Where, FRS EV It is a flexible backup for electric vehicles. EV and FRS req It is the hill climbing and flexible reserve requirement for electric vehicles.

9. The method for real-time day-ahead optimization scheduling of virtual power plants based on improved Harris Hawk optimization according to claim 1 is characterized in that: The power balance in the virtual power plant in step (3) is: In the formula, For equivalent output power, For power exchange between the grid and the VPP, is the efficiency of the fuel cell, is the electricity generated by wind turbines, It is solar photovoltaic power generation. is the available power of the EV as a flexible reserve, is the available power from the ESS as spinning reserve, For power load demand.

10. The method for real-time day-ahead optimization scheduling of virtual power plants based on improved Harris Hawk optimization according to claim 1 is characterized in that: The improved Harris Hawk optimization algorithm in step (4) is: To make the search space more diverse, the eagle step size is increased, r1 and r3 are selected from four random numbers, and they are modeled using an exploration factor, which is: According to the exploration coefficients, due to some changes in r1 and r3, the vector position of the search body in subsequent iterations is updated to: A chaotic sequence is used to make the distribution of random numbers more uniform; this mapping is suitable for enhancing the initial position of the search agent, which is expressed as: Where, X i+1 represents the position of the new eagle after mapping, a is the parameter set by the algorithm test, and the random walk strategy is selected to improve the local optimal trap and sort the same fitness value as the iteration proceeds, and continue to move forward; activate a variable parameter to establish synchronization with the position of the search agent, which is expressed as: Where, X n (i) is the new position after applying the random walk. By retaining the solution with better performance, the next subsequent iteration will be performed according to formula (63):

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