Multi-objective Optimal Dispatch Research Articles

Multi-Objective Interval Optimization Dispatch of Microgrid via Deep Reinforcement Learning

Multi-Objective Interval Optimization Dispatch of Microgrid via Deep Reinforcement Learning

Multi-Objective Short-Term Optimal Dispatching of Cascade Hydro–Wind–Solar–Thermal Hybrid Generation System with Pumped Storage Hydropower

Aiming to mitigate the impact of power fluctuation caused by large-scale renewable energy integration, coupled with a high rate of wind and solar power abandonment, the multi-objective optimal dispatching of a cascade hydro–wind–solar–thermal hybrid generation system with pumped storage hydropower (PSH) is proposed in this paper. Based on the proposed system, the scheduling operation strategy takes into account the complex restrictions of cascade hydropower as well as the flexibility of the PSH. According to various scenarios, the NSGA-II approach is adopted to address the optimization problem, minimizing the system’s residual load variation and operation cost. The Pareto solution sets are contrasted and evaluated, applying the TOPSIS with CRITIC weighting. Additionally, the scheduling output of thermal power, cascade hydropower, and PSH is given in terms of different scenarios. The results demonstrate that the allocation of PSH to a hybrid energy system can significantly reduce the operation cost and the fluctuation in the residual load.

Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior

Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior

Multi-objective optimal dispatch strategy for power systems with Spatio-temporal distribution of air pollutants

Accurate and effective pollutant emission control models are key in mitigating the environmental impact of energy supply systems. This paper proposes a novel high-dimensional multi-objective optimal dispatching strategy for power systems considering the spatial and temporal distribution of multiple pollutants. Firstly, a spatial and temporal distribution model of pollutants in thermal power plants is constructed, which is truly applicable to power dispatch. For modeling the pollution situation, the daily changes in the atmospheric boundary layer are taken into account, fully reflecting the pollutant dispersion characteristics of thermal power plants and improving the result accuracy. Next, a high-dimensional multi-objective optimization model is developed to simultaneously reduce the cost of power generation, carbon emission and the impact air quality from VOCs (volatile organic compounds), SO2 and NO2. This model combines the spatial and temporal distribution characteristics of various pollutants and environmental capacity. Finally, a representative high-dimensional multi-objective optimization algorithm is employed to obtain an approximate Pareto-optimal solution set. And a multi-objective decision method is proposed to filter the compromise solution. The results show that the APCs of VOCs, SO2 and NO2 are reduced by 27.5%, 10.13% and 23.16%, respectively, in the pollution day model. On non-pollution days, the proposed scheduling method not only effectively improves air quality, but also achieves cost savings of $1276.59 and reduces CO2 emission by 0.168 × 104t compared to the emission limitation method. The proposed scheduling method can not only effectively improve air quality, but also make corresponding adjustments according to the spatial and temporal changes of environmental capacity, which can truly realize economic and environmental-friendly power scheduling.

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.

Multi-objective optimization scheduling of integrated energy system interval under multiple uncertainty environment

An interval multi-objective optimization dispatch approach of integrated energy system under various uncertain variables is presented to address the impact of uncertainty on dispatch plan. First, interval mathematics is used to describe multiple uncertain factors in the system, such as efficiency of multi-energy coupling unit, demand response, and electricity substitution; second, considering the efficient use of energy, the economics of system operation, and difference in energy quality, optimize system operation with goal of lowest system operating cost and highest exergy efficiency; and finally, based on interval multi-objective linear programs, optimize system operation Case research reveals that multi-objective optimization scheduling may account for system operating economics and exergy efficiency and increase the system’s capacity to handle numerous uncertainties.

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.

Optimization Technique for Renewable Energy Storage Systems for Power Quality Analysis with Connected Grid

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function’s preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system’s performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation’s results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode.

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.

Optimal dispatch of hydrogen/electric vehicle charging station based on charging decision prediction

The hydrogen/electric vehicle charging station (HEVCS) is widely regarded as a highly attractive system for facilitating the popularity of hydrogen and electric vehicles in the future. However, conventional optimal dispatch of HEVCS could lead to poor performance due to the lack of adequate consideration of vehicle charging decision behaviours and neglection of the impacts of different information sources on it. This paper investigates a charging demand prediction method that considers multi-source information and proposes a multi-objective optimal dispatching strategy of HEVCS. First, an information interaction framework of integrated road network, vehicles and HEVCS is introduced. Road network model and HEVCS model are established based on the proposed framework. To improve the flexibility of dispatch, two charging modes are designed, which are intended to guide drivers to adjust their consumption behaviour by electricity price incentives. Furthermore, psychologically based hybrid utility-regret decision model and Weber-Fechner (W–F) stimulus model are developed to reasonably predict drivers' choice of charging stations and charging modes. The daily revenue of HEVCS and the total queuing time of drivers are the objective functions considered in this paper simultaneously. The above multi-objective optimization results that the proposed strategy can effectively improve the benefits of HEVCS and reduce energy waste. Additionally, this paper discusses the results of a sensitivity analysis conducted by varying incentive discount, which reveals the combined benefits of the HEVCS and the vehicles are effectively increased by setting reasonable incentive discounts.

Improved gravitational search algorithm and novel power flow prediction network for multi-objective optimal active dispatching problems

Multi-objective optimal active dispatching (MO-OAD) is a high-profile topic in power grid optimizations. Traditional methods are insufficient for the MO-OAD problem due to its nonconvexity and strict constraints. Therefore, an improved multi-objective gravitational search algorithm (IMGSA) with better population diversity and search capability is put forward in this paper. Five MO-OAD experiments considering various user requirements demonstrate that the IMGSA achieves preferable Pareto-front (PF) over the non-dominated sorting genetic algorithm-II (NSGA-II) and other recently published algorithms. More importantly, to further explore potential elite schemes after obtaining the best compromise scheme (BCS) of IMGSA, an efficient power flow prediction model based on the radial basis function (RBF) network is proposed for the first time. By accurately mapping the nonlinear relationship between the control variables of MO-OAD and optimization objectives, more than five elite dispatching schemes are determined by the suggested RBF prediction model. These elite schemes greatly reduce the fuel cost, power loss and exhaust emission of different IEEE power systems. Quantitative evaluations such as generational distance (GD) and hyper-volume (HV) indicators prove that the proposed IMGSA with RBF prediction model (IMGSA-RBF) is more competitive than many existing algorithms. Detailed MO-OAD experiments show that IMGSA-RBF method has significant advantages in PF-uniformity, PF-diversity, and the quality of optimal dispatching schemes. In general, the innovative IMGSA-RBF method provides a valuable technology to realize desirable power grid operation with less carbon emission and better economy.

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 virtual power plants considering source-load uncertainty in V2G mode

To solve the risks brought by the uncertainty of renewable energy output and load demand to the virtual power plant dispatch, a multi-objective information gap decision theory (IGDT) dispatching model for virtual power plants considering source-load uncertainty under vehicle-to-grid (V2G) is proposed. With the lowest system operating cost and carbon emission as the optimization objectives, the multi-objective robust optimization model for virtual power plants is constructed based on the uncertainties of wind output, photovoltaic output and load demand guided by the time of use price. The weights of uncertainties quantify the effects of uncertainty factors. The adaptive reference vector based constrained multi-objective evolutionary algorithm is used to solve it. The weight coefficients, evasion coefficients of uncertainties and the penetration rate of electric vehicles are analyzed for the optimal dispatching of the virtual power plant. The algorithm results show that the method can effectively achieve load-side peak shaving and valley filling and has superiority in terms of economy, environmental benefits, robustness and stability.

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.

Multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty of renewable energy generations based on two-level iterative strategy

In this study, multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty in renewable energy generations is designed to obtain optimal energy point sets of microgrid. The proposed dispatching model contains upper-level model and lower-level model. The economic and environmental benefits of the microgrid are adopted as the optimization objectives in the upper-level model and the economic benefits under incentive-based demand response is taken as the optimization objective in the lower-level load demand. In the upper-level model, sequence operation theory is designed to deal with the uncertainty in renewable energy generations, and virtual real-time tariffs and electricity credibility of power consumption are utilized to make demand response by shifting load in the lower-level model. To optimally solve the proposed model, improved enhanced multi-objective optimization sparrow search algorithm (EMOSSA) based on sinusoidal search strategy, diversity variant processing by Cauchy variation and external archive updating mechanism is developed to optimize the objective functions of the upper-level model and mixed integer linear programming CPLEX is employed to determine the load demand for the lower-level model. After that, the power demand plan of the lower-level model and the real-time prices of the upper-level model are updated iteratively to obtain the optimal dispatching solutions using the real-time pricing mechanism. Eventually, experimental results of several operation scenarios illustrate the proposed optimal dispatching method can deal with the uncertainty of renewable energy generations and the randomness of load, thus, achieving a well supply and demand balance between microgrid and users.

Multi objective optimal reactive power dispatch for enrichment of power system behavior using modified ant lion optimizer

Multi objective optimal reactive power dispatch for enrichment of power system behavior using modified ant lion optimizer

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.

Research on Multi-objective Optimal Dispatch of Power System Based on Demand Response

Due to the fluctuation of charge demand at the user side, the rationality of power system operation control with a single parameter setting will be reduced. Therefore, research on multi-objective optimal dispatching of power systems based on demand response is proposed. Firstly, the optimal configuration model of the power system is constructed from the perspectives of energy utilization rate and load demand at the user side. Then, the parameters in the actual executable threshold range are substituted into the model with the balance relationship between the output power of each unit as the constraint. Finally, the parameter setting result with the highest fitting degree between the system and the objective function is calculated and taken as the dispatching objective. The test results show that under the control of the dispatching method designed in this paper, the output of the power system always keeps a high fit with the actual load demand, which has high rationality.

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.