Multi-objective Optimal Dispatch Model Research Articles

Multi-objective confidence gap decision based robust optimal dispatch of integrated energy system using entropy expectation maximization GMM

To cope with the uncertainty of renewable energy output and achieve robust optimal dispatch of the integrated energy system, the confidence gap decision theory with Gaussian mixed probability distribution is introduced into the dispatch model. The entropy expectation maximization Gaussian mixture model (GMM), which is developed by adding an entropy factor to the expectation maximization algorithm, is proposed to reduce the initial value sensitivity in the GMM hidden variables solution process. Considering the comprehensive optimization objectives of maximizing exergy efficiency and minimizing operating cost, a multi-objective robust optimal dispatch model based on the confidence gap decision is established. And the model is transformed into a deterministic model according to the uncertainty theory for the sake of convenient calculation. Further, an efficient non-dominated sorting mechanism is combined with an improved differential evolution algorithm to efficiently solve the model. Finally, the effectiveness and superiority of the proposed method are verified by example analysis.

Multi-objective optimal dispatch of integrated heat and electricity energy systems considering heat load energy quality

As the energy crisis and consumption revolution advance, traditional energy service providers gradually transform into customer-centric integrated energy service providers. However, the customer’s experience with energy depends on the quality. This paper proposes an energy quality model for heat load of the integrated heat and electricity energy systems, from which the derived energy quality factor for heat load participates as one of the objectives of the multi-objective optimization model. The considered heat transfer dynamics accurately describe the heat transmission through the heat network. Finally, the effectiveness of the proposed multi-objective optimal dispatching model is verified by numerical simulations, in which a decision-making model established based on exergy economics enables the selection of a compromise solution from the Pareto front as the basis for dispatching.

Control Technology of Smart Grid Considering Intermittent Power Uncertain Dispatch

The phenomenon of “anti-peak regulation” occurs when large-scale new energy is connected to the grid. The strange phenomenon of wind, light, and water curtailment at low loads occurs in areas with weak power regulation capacity, and the problem of peak regulation of the power system has become increasingly prominent. The typical scenario method is adopted to simplify the description of photovoltaic power generation output and hydropower station runoff. Then, considering the fluctuation of power production and output of the water and photovoltaic complementary system, the short-term multi-objective optimal dispatching model of the water and the photovoltaic complementary system is constructed with the objective of maximizing the average capacity coefficient and power generation load rate of the system on the premise of meeting the corresponding hydropower constraints. Finally, the simulation experimental platform is built. The multi-objective algorithm is used to solve the model. Through the case study, the complementary effect of intermittent power uncertain dispatching is analyzed, and the effectiveness of the model is verified. The dispatching mode can fully guarantee the collaborative optimization decision-making problem of a multi-microgrid with clean power. It can also effectively promote the acceptance level of clean power in the distribution network, and reduce the carbon emission of the distribution network.

Robust Multi-objective optimal dispatching model for a novel island micro energy grid incorporating biomass waste energy conversion system, desalination and power-to-hydrogen devices

To realize renewable and self-sustainable energy supply in island region, based on geographical characteristics with abundant renewable resources, an optimal model for island micro energy grid (MEG) is designed incorporating biomass waste energy conversion system (ECS), desalination, and power-to-hydrogen (BSP-MEG) Firstly, the mathematical model is designed, including models of power generators, ECS, desalination and power-to-hydrogen (P2H) devices, etc. Next, the multi-objective scheduling optimization model is designed, containing conventional scheduling model (Scheduling optimization objectives and constraints established with minimum operation and environment costs) and stochastic scheduling model (Minimum Conditional Value-at-Risk objective specific to volatility and uncertainty of renewable generations based on robust stochastic optimization method). Then, to solve the multi-objective optimization problem (MOP), a hybrid differential evolution algorithm is proposed based on local optimal and external archiving strategies. Finally, the MEG of YongXing Island is selected as an example. The results show (1) BSP-MEG effectively realized multi-energy cooperative optimization, and promote intra-day peak shaving. (2) BSP-MEG reduced operating costs, environmental costs and Conditional Value-at-Risk (CVaR) by 78.2%, 61.8% and 77.9% respectively, while curtailment rate by 25.6 to 0.9%. (3) Whether in general scenario or worst, BSP-MEG can realize self-production and self-sale of energy and material, of which risk resistance ability is better. (4) By designing local optimal and external archiving strategies, hybrid differential evolution algorithm performs better in solving complex MOP. In general, the optimization model proposed in this paper can improve the utilization of renewable resources, alleviate the shortage of fresh water, and help realize renewable and sustainable energy supply.

Multiobjective Optimal Dispatch of Mobile Energy Storage Vehicles in Active Distribution Networks

In active distribution networks (ADNs), mobile energy storage vehicles (MESVs) can not only reduce power losses, shave peak loads, and accommodate renewable energy but also connect to any mobile energy storage station bus for operation, making them more flexible than energy storage stations. In this article, a multiobjective optimal MESV dispatch model is established to minimize the power loss, renewable energy source curtailment, and total operating cost of ADNs. Additionally, a method to directly obtain the compromise optimal solution (COS) of the Pareto optimal solutions (POSs) of a three-objective optimization problem is proposed. Based on the normalized normal constraint (NNC) method for solving the POSs of a three-objective optimization problem, a bilevel optimization model is established to directly obtain the COS. The innerlayer model uses the NNC method to calculate the POS corresponding to a point on the Utopia plane, and the outerlayer model uses the improved Nelder–Mead (INM) method to directly search the POS with the minimum distance to the Utopia point as the COS. Compared with the traditional method, which first solves the POSs of the three-objective optimization problem and then selects the COS, the proposed INM method has higher computational efficiency and obtains a better COS. Finally, case studies on the modified IEEE-33 bus distribution network and an actual 180 bus distribution network demonstrate the effectiveness of the proposed method.

Multi-objective optimal dispatching of combined cooling, heating and power using hybrid gravitational search algorithm and random forest regression: Towards the microgrid orientation

In order to eventually reach the net-zero carbon target, the low carbon transition demands substantial growth in the penetration of renewable generation in energy systems. It is essential to have enough flexible resources to prevent the curtailment of energy systems with high intermittent renewable output to run consistently. Therefore, this study focuses on the multi-objective optimal dispatching model of combined cooling, heating, and power (CCHP) microgrid with power-to-gas (P2G). The structure of the CCHP microgrid and the working principle of P2G are studied and analyzed. First, briefly described the multi-objective optimal dispatch model of the CCHP microgrid with P2G established with the goal of operational and environmental cost. Second, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) are introduced to find out the optimal values. The proposed model verifies that P2G improves the wind abandonment capacity of the CCHP microgrid operation system. Furthermore, it reduces environmental costs and transforms environmental values into economic benefits during system operation. Noting that, the GSA-RFR improves the system economy by 7.13% comparatively.

Dispatching Strategy for Low-Carbon Flexible Operation of Park-Level Integrated Energy System

In the face of the dual crisis of energy shortages and global warming, the vigorous development of renewable energy represented by wind-solar energy is a significant approach towards achieving energy transition, carbon peaking, and carbon neutrality goals. Targeting the park-level integrated energy system (PIES) with high penetration of wind-solar energy, we propose a day-ahead dispatching strategy that takes into account the flexible supply and the reward-punishment ladder-type carbon trading mechanism (RPLTCTM). Firstly, RPLTCTM and carbon capture equipment (CCE) are considered in the dispatching model, and the mechanism of coordinated operation of CCE and RPLTCTM is explored to further improve the system’s ability to restrain carbon emissions. Secondly, power-based flexibility indicators (PFIs) are adopted to quantitatively evaluate the flexibility supply, and based on the load demand response characteristics, the dispatchable resources on the load side are guided to improve the system’s operation flexibility. On this basis, a multi-objective optimal dispatching model that takes into account the carbon emission cost, energy cost, and flexibility supply are constructed, and the original problem is transformed into a mixed-integer single-objective linear problem through mathematical equivalence and flexibility cost. Finally, simulation examples validate that the economy, flexibility, and low-carbon level of the dispatching plan can be synergistically improved by the proposed strategy.

Multi-Objective optimal scheduling of island microgrids considering the uncertainty of renewable energy output

The grid connection of wind power and photovoltaics significantly increases the uncertainty of the operation and scheduling of power systems. The maximum power that can be transmitted in a particular time slot often fluctuates randomly around a certain value according to a certain probability distribution, and a prediction error occurs between the predicted and actual power. When the actual maximum power that wind power and photovoltaics can generate is less than the power arranged in the scheduling plan, a significant increase in operating costs will occur, as well as an increase in the frequency fluctuation of the microgrid. This may harm the system. To study the effects of the uncertainty of renewable energy output on microgrid dispatching, this paper divides power sources into basic load and frequency modulated (FM) power sources according to the FM characteristics and establishes a multi-objective optimal dispatching model of island microgrids considering the uncertainty of renewable energy output, using the economic benefit and frequency fluctuation rate of the microgrids as the optimization target. Additionally, this paper proposes a two-step optimal scheduling method based on power classification: First, the Monte Carlo method is used to estimate the expectation and variance of the objective function, and a certain number of samples are obtained through sampling. Second, the particle swarm optimization algorithm is used to determine the installation plan of the FM and base load power sources to minimize the average sample cost. Finally, a backpropagation neural network is used to construct the frequency modulation strategy of FM power sources. An analysis with examples shows that, in the context of many samples, the model and proposed two-step scheduling method are reasonable and effective.

Multiobjective Economic Optimal Dispatch for the Island Isolated Microgrid under Uncertainty Based on Interval Optimization

In order to analyse the impact of renewable generation and load uncertainties on the economic operation optimization of the island microgrid, a multiobjective economic optimal dispatch model under uncertainty based on interval optimization is proposed in this paper. The mathematical model of distributed generation and the prediction model of wind speed and wave generation are established. The uncertainties of renewable generation and load are described by the interval mathematical method. On this basis, the interval multiobjective optimal dispatch model is presented. For the “battery disgusting” users on the island, the battery cost is regarded as a separate optimization objective, and a multiobjective optimization objective function to minimize the economic cost, battery cost, and pollution emission of the island microgrid is discussed. An island microgrid, composed of wind turbine, photovoltaic, wave energy generation, diesel generator, and energy storage system, is chosen as a case study. The NSGA-II algorithm is applied to solve the multiobjective optimal problem. The results for deterministic forecast data and load are analysed, and the optimal operation scheme is obtained by the improved multiobjective grey target decision-making method. The influence of renewable generation fluctuations ±10%, ±20%, and ±30% and the load fluctuations ±10% and ±20% on island microgrid operation optimization is discussed in detail, respectively. The relevant research results can provide a reference for formulating the operating scheme of the island microgrid.

V2G Multi-Objective Dispatching Optimization Strategy Based on User Behavior Model

V2G (Vehicle to Grid) technology can adjust the grid load through the unified control of the charging and discharging of electric vehicles (EVs), and achieve peak shaving and valley filling to smooth load fluctuations. Aiming at the random and uncertain problem of EV users travel and behavior decision-making, this paper proposes a V2G multi-objective dispatching strategy based on user behavior. First, a V2G behavior model was established based on user behavior questionnaire surveys, and the effective effect of EV load was simulated through Monte Carlo simulation. Then, combined with the regional daily load curve and peak-valley time-of-use electricity prices, with the goal of stabilizing grid load fluctuations and increasing the benefits of EV users, a multi-objective optimal dispatching model for EV clusters charging and discharging is established. Finally, Considering the needs of EV users and the operation constraints of the microgrid, the genetic algorithm is used to obtain the Pareto optimal solution. The results show that when dispatching with the maximum benefit of users, the peak-to-valley ratio of the grid side can be reduced by 2.99%, and the variance can be reduced by 9.52%. The optimization strategy can use peak and valley time-of-use electricity prices to guide the intelligent charging and discharging of EVs while meeting user needs, so as to achieve the optimal multi-objective benefit of V2G participation in power response.

Combined Dispatching of Hydropower and Wind Power Based on the Hedging Theory

In order to improve the utilization rate of water resources in the flood season of the reservoir effectively and promote wind power consumption, this paper proposes an optimization model for the combined dispatching of wind power and hydropower based on the hedging theory. First, the conflicting relationship between the water storage benefits of hydropower stations, flood control risks, and the joint output of hydropower and wind power in joint dispatching is studied. The introduction of hedging theory divides the combined dispatching of wind power and hydropower into a two-stage dispatching problem including the decision-making stage and the remaining stage; Second, considering the uncertainty of water forecasting and wind power forecasting, a multi-objective optimal dispatching model of hydropower and wind power based on hedging theory is constructed. This model aims to minimize flood control risks, maximize water storage benefits, and minimize wind power and hydropower combined power output volatility. Finally, the non-dominated sorting genetic algorithm (NSGA2) is used to solve the specific examples. The results show that the model built in the article controls the flood control risk at each time period not to be higher than 1.63 × 10−3 (the flood control standard corresponding to the flood control risk in 50 years is 0.006). Additionally, the water level of the reservoir increased from the flood limit water level (583.00 m) to 583.70 m. It greatly increases the water storage capacity and effectively improves the utilization rate of water resources. At the same time, the optimized scheduling scheme reduced the peak-valley difference of joint output from 125.00 MW to 35.66 MW, and the peak-valley difference was greatly reduced. It effectively improves the volatility of wind power. The validity of the model is verified, and the obtained scheme can provide decision-making for the joint dispatch scheme of hydropower and wind power.

Study on Economic Operation and Optimal Dispatching of VPP

Large scale grid-connected wind power will have a great impact on the power grid. In order to ensure the safetyof power grid operation, it is necessary to quickly adjust the load of thermal power plants to stabilize the fluctuation ofwind power output. The economy of thermal power unit is poor in low load operation, and the safety and service life of theunit will be reduced when the load is adjusted frequently. In order to solve the problems, wind and thermal powercomplementarity is proposed, and the wind farm and thermal power plant constitute a virtual power plant (VPP) toparticipate in the grid dispatching. A multi-objective optimal dispatching model of VPP considering economy and rapidity isestablished, which is solved by improving genetic algorithm. The results show that the multi-objective optimal dispatchingmodel can make the economy of VPP the best on the premise of meeting the requirements of grid dispatching time throughthe simulation of differentscenarios. Furthermore, the strategy of selecting the weight coefficient of multi-objectiveoptimization model is given, and the wind power consumption capacity of VPP is analysed.

Multiobjective optimal dispatch of microgrid based on analytic hierarchy process and quantum particle swarm optimization

Owing to the rapid development of microgrids (MGs) and growing applications of renewable energy resources, multiobjective optimal dispatch of MGs need to be studied in detail. In this study, a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines, photovoltaics, diesel engine unit, load, and battery energy storage system. The economic cost, environmental concerns, and power supply consistency are expressed via subobjectives with varying priorities. Then, the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives. The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG. Finally, the validity of the proposed model and solution methodology are confirmed by case studies. This study provides reference for mathematical model of multiojective optimization of MG and can be widely used in current research field.

Optimal Dispatch of High-Penetration Renewable Energy Integrated Power System Based on Flexible Resources

The volatility and uncertainty of high-penetration renewable energy (RE) challenge the stability of the power system. To tackle this challenge, an optimal dispatch of high-penetration RE based on flexible resources (FRs) is proposed to enhance the ability of the power system to cope with uncertain disturbances. Firstly, the flexibility of a high-penetration RE integrated power system is analyzed. The flexibility margin of power supply and flexible adaptability of RE are then introduced as the evaluation indices for optimal operation. Finally, a multi-objective optimal dispatch model for power system flexibility enhancement based on FRs under the constraint of flexibility indices is proposed. The simulation results show that the proposed optimal dispatch can effectively enhance the flexibility of the power system and the penetration of RE and reduce pollutant emissions. Compared with the conventional method, the daily average emissions of CO2, SO2, and NOx with the proposed method are reduced by about 83,600 kg, 870 kg, and 370 kg, respectively, the maximum allowable volatility of net load is increased by 7.63%, and the average volatility of net load is reduced by 2.67%.

Distribution System Renewable Hosting Capacity Maximization with Second-Use Electric Vehicle Storage Using Critical Capacity Retention Calculation Model

The continuous increase of the penetration of distributed generation in the distribution network poses more severe challenges for its full accommodation. In this context, an energy storage system can be installed to enhance the ability to accommodate renewable energy because of its high flexibility. However, the investment cost of brand new energy storage equipment is so high that the widespread application of storage system in the distribution system has been limited. This article proposes a method, which aimed at optimizing energy storage dispatching in a distribution network, and takes the use of second-use electric vehicles (EV) batteries into account. A calculation model of power battery second-use capacity was established, the upper and lower bounds of the initial capacity of second-use energy storage system (SUESS) can be determined after the reorganization of the retired batteries, and then a multi-objective optimal dispatching model for the distribution system with SUESS can be established with reference to the capacity attenuation model and the daily mileage statistics model of the electric vehicle. The case study results show that configured SUESS in the distribution network can not only meet the needs of the distribution network to fully accommodate distributed generation, but also delay the upgrade of the distribution network.

Multi-objective optimal dispatching of microgrid based on improved genetic algorithm

Aiming at the optimization problem of micro-grid energy dispatch, the environmental benefit, system operation and maintenance cost, start-stop cost and main grid electricity price are proposed as the objective function, and the influence of the actual operation constraints of micro-grid system and the distributed non-scheduled new energy are taken into account at the same time. The multi-objective optimal dispatching model of microgrid is constructed, and the model is solved based on improved genetic algorithm. The numerical simulation results show that compared with the traditional single-target scheduling method of microgrid, the optimal scheduling method of this paper is beneficial to the micro-grid in the process of scheduling decision-making to achieve the overall coordination of economic, environmental and social benefits of microgrid.

Incorporating energy storage and user experience in isolated microgrid dispatch using a multi‐objective model

In order to coordinate multiple different scheduling objectives from the perspectives of economy, environment and users, a practical multi-objective dynamic optimal dispatch model incorporating energy storage and user experience is proposed for isolated microgrids. In this model, besides Microturbine units, energy storage is employed to provide spinning reserve services for microgirds; and furthermore, from the perspective of demand side management, a consumer satisfaction indicator is developed to measure the quality of user experience. A two-step solution methodology incorporating multi-objective optimization (MOO) and decision analysis is put forward to address this model. First, a powerful heuristic optimization algorithm, called the {\theta}-dominance based evolutionary algorithm, is used to find a well-distributed set of Pareto-optimal solutions of the problem. And thereby, the best compromise solutions (BCSs) are identified from the entire solutions with the use of decision analysis by integrating fuzzy C-means clustering and grey relation projection. The simulation results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab microgrid test system demonstrate the effectiveness of the proposed approach.

Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles

Abstract Large-scale uncoordinated charging of electric vehicles (EVs) will become a reality in the near future, which will have a great impact on the stability and security of power system operation. In this regard, this paper proposes a multi-objective optimal load dispatch model of microgrid with the stochastic access of EVs. The uncertainties of EVs are modeled by using the Monte Carlo simulation. The objective function of the model includes the operating cost, pollutant treatment cost and load variance. Distributed generations (DGs) are considered in the model, including photovoltaic (PV) array, wind turbine (WT), diesel engine (DE) and micro turbine (MT). In order to solve the proposed model effectively, an improved particle swarm optimization (PSO) algorithm is proposed. Then we discuss the dispatch results under three different scheduling scenarios, i.e., uncoordinated charging scenario, coordinated charging scenario with and without DGs. The simulation results show that charging loads will be shifted form high-priced periods to low-priced periods under the coordinated charging mode of EVs, which can reduce daily costs by 3.09% and effectively improve the stability of power system operation. Meanwhile, the penetration of DGs can further reduce 6.43% of total cost by managing output of DGs. Further, the influence of cost weight factor on dispatch results is discussed. It illustrates that the cost weight factor is a trade-off between the total cost and the load variance. The experimental results demonstrate the effectiveness of the model under different charging situations.

Multi-Objective Optimal Dispatch Considering Wind Power and Interactive Load for Power System

With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.

Optimal Dispatching of Active Distribution Networks Based on Load Equilibrium

This paper focuses on the optimal intraday scheduling of a distribution system that includes renewable energy (RE) generation, energy storage systems (ESSs), and thermostatically controlled loads (TCLs). This system also provides time-of-use pricing to customers. Unlike previous studies, this study attempts to examine how to optimize the allocation of electric energy and to improve the equilibrium of the load curve. Accordingly, we propose a concept of load equilibrium entropy to quantify the overall equilibrium of the load curve and reflect the allocation optimization of electric energy. Based on this entropy, we built a novel multi-objective optimal dispatching model to minimize the operational cost and maximize the load curve equilibrium. To aggregate TCLs into the optimization objective, we introduced the concept of a virtual power plant (VPP) and proposed a calculation method for VPP operating characteristics based on the equivalent thermal parameter model and the state-queue control method. The Particle Swarm Optimization algorithm was employed to solve the optimization problems. The simulation results illustrated that the proposed dispatching model can achieve cost reductions of system operations, peak load curtailment, and efficiency improvements, and also verified that the load equilibrium entropy can be used as a novel index of load characteristics.