Multi-objective Optimization Scheduling Model Research Articles

Research on day-ahead and intraday scheduling strategy of distribution network based on dynamic partitioning

With the massive participation of new energy in the operation of distribution network (DN), the uncertainties arising from both load and renewable generation have brought challenges to its secure and reliable operation. In this paper, a model of day-ahead and intraday optimal scheduling based on dynamic partitioning (DP) of DN is constructed to realize the refinement of operation in both temporal and spatial dimensions, as well as to improve the reliability of system operation. For the strong fluctuation characteristics of the source-load in DN, the DP is achieved under the premise that the partition structure in each period is tight and the internal power supply has the ability to balance the power fluctuations. In order to improve the regional autonomy of DN, a day-ahead multi-objective optimal scheduling model is constructed based on the DP with the optimization objectives of weak inter-partition power coupling, lowest deficiency rate of flexibility, and lowest operating cost. And a scheme for articulation is proposed to ensure the effective guidance of day-ahead for intraday. Then the intraday dispatch model is constructed by shifting the focus to the incorporate characteristics of DP and quickly smooth the net load fluctuations. Finally, the performance and reliability of the proposed models are verified via case studies.

Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850.

Multi-objective optimization scheduling and flexibility margin study in integrated energy systems based on different strategies

Integrated Energy Systems (IES) serve as an effective solution to address grid fluctuations. However, mainstream IES still adhere to established supply strategies such as thermal and electrical non-conversion (TENC), heat determined by power (HDP), and power determined by heat (PDH). These strategies overlook the additional flexibility generated by the synergy between electricity and heat, preventing the full realization of their advantages in supporting the grid. This paper proposes a Flexible Scheduling (FS) operational strategy for IES that maximizes the utilization of flexibility resources to meet the demands of heating, cooling, and electricity while maintaining grid stability. The superiority of this strategy is analyzed through an IES multi-objective optimization scheduling model and a comprehensive flexibility margin model. The results of a case study indicate that the FS strategy reduces total costs by 19.9% compared to TENC, 16.6% compared to HDP, and 5.2% compared to PDH. It exhibits the strongest support for the grid, increasing the overall flexibility margin of IES by 40.5% compared to TENC, 12.7% compared to HDP, and 8.2% compared to PDH. The FS-based IES operational strategy proposed in this paper contributes significantly to the flexible and economical operation of IES.

Research on the Quantitative Evaluation of the Three-Dimensional Seismic Resilience of Power Systems Based on an Improved Genetic Algorithm

ABSTRACT In this study, a three-dimensional (3D) performance function is established to evaluate 3D seismic resilience, and a multiobjective optimization scheduling model based on node importance and repair order evaluation indicators is developed. On the basis of sequential coding, considering the number of repair teams, the concept of a “repair team vector” is proposed, and an improved genetic algorithm based on the Pareto dominance relationship and an elite file strategy is introduced to screen for the optimal postearthquake repair scheduling scheme. The results demonstrate that the developed optimization scheduling model yields improved computational accuracy and speed in the case evaluations.

Short-term optimization scheduling method of cascade hydropower and photovoltaic complementary system based on pumping station operation strategy

With the rapid development of photovoltaic power generation, how to improve the photovoltaic grid connection rate is an urgent problem to be solved. This article proposes an optimized scheduling method for the water and photovoltaic complementary system, taking into account the operation strategy of pump stations to improve the photovoltaic grid connection rate. Firstly, a multi-objective optimization scheduling model is constructed to consider both power generation and output fluctuation, and the uncertainty of photovoltaic power generation is analyzed from multiple perspectives. Then, taking the cascade hydropower stations and surrounding photovoltaic power stations in a river basin in Sichuan as an example, the operation strategy of pump stations is introduced into the water and photovoltaic complementary system, considering different weather scenarios, to reduce the photovoltaic curtailment rate. The study verifies that the introduction of pump stations can effectively increase the photovoltaic grid connection rate, and quantitatively analyzes the pump station capacity configuration under different photovoltaic penetration rates.

A multi-objective segmentation scheduling method for the flexible dismantling of retired power batteries taking into account transfer time

Decommissioned power batteries are prone to dilapidated deformation, which makes it difficult to carry out unified and efficient production scheduling for disassembly production. In this paper, a multi-objective scheduling method applied to the dismantling workshop of retired power batteries is presented. For the characteristics of power battery dismantling, a strategy of scheduling by a battery pack and battery module in stages is proposed. The multi-objective optimal scheduling model of the dismantling workshop is established with the decision objectives of reducing the time of completion, energy dissipation and workstation load, taking into account the transfer time of batteries in the dismantling process, and is solved by genetic algorithm. The chromosome coding method, the crossover variation method and the elite retention strategy in the genetic algorithm were optimized. The method is applied to the dismantling examples of 5 kinds of power batteries, and the simulation results show that the proposed method is feasible and effective.

An advanced multi-objective collaborative scheduling strategy for large scale EV charging and discharging connected to the predictable wind power grid

With the large-scale EV connected into the wind power grid, the intermittent, fluctuating and stability bring rigorous challenges for power quality and dispatch, thus wind curtailment becomes more serious. This paper proposes an advanced multi-objective collaborative scheduling strategy to improve the accuracy of the predictable wind grid and ensure the stable operation of the grid by optimizing wind power and EV synergistically. Firstly, a hybrid power prediction method is proposed based on density-based spatial clustering applied to noise (DBSCAN) and partial least squares regression (PLSR) combined with long and short-term memory neural network (LSTM). It establishes a linear component prediction mode to predict the nonlinear wind power efficiently. Secondly, a multi-objective optimization scheduling model is established according to the predicted wind power, considering the vehicle-to-grid (V2G) characteristics of EV, grid load fluctuations, wind curtailment capacity, EV charging costs, and grid losses. The global control aims to achieve overall energy balance and optimize the charging and discharging of EV in a time dimension under constrained conditions. Additionally, based on the total number of EV charging and discharging determined by the global level control, a spatial scheduling optimization is conducted for EV and wind power by the distribution network control, with the goal of minimizing network losses in the distribution network system. Finally, through simulation and comparison of four scenarios, the results demonstrate that the proposed method achieves synergistic optimization of wind power and EV. This paper provides an efficient solution for the optimized scheduling of large-scale EV connected to the predictable wind power grid.

Optimal Dispatching of Distributed Generation Cluster Systems Considering Multiply Optimization Objectives

In the trend of increasing energy shortages and environmental pollution, integrating clean micro sources as cluster systems has become the mainstream trend in the DG cluster system research field. In an effort to ensure the economy and environmental friendliness of Distributed Generation (DG) cluster systems, a multi-objective optimization scheduling model is established to minimize the operating and environmental costs of DG cluster systems. Based on a multi-objective particle swarm optimization algorithm, this paper conducts a study on the capacity optimal configuration of wind turbines, photovoltaic generators, and energy storage devices. The model proposed in this paper is applied to analyse a DG cluster system in a rural area dominated by agriculture. The results show that the proposed model has a significant effect in reducing the operating and environmental costs of the DG cluster system.

Multi-objective optimal scheduling model for shared bikes based on spatiotemporal big data

Station-free bike sharing is one of the most important short-distance means of transportation. However, with the surge in the number of shared bikes, an imbalance of supply and demand in time and space caused by the disorderly parking of shared bikes has also emerged. This research combined massive spatiotemporal trajectory data of shared bikes and user travel demands to propose a feasible multi-objective optimal scheduling method. Specifically, this research presented a model that utilizes extensive order data to analyze user travel patterns, divides shared bike operation areas into internally connected communities by Geohash coding, and analyzes the shared bike dispatch hotspots in each community segment. Then, a novel model for multi-objective optimal scheduling of shared bikes was proposed based on NSGA-II. The model takes the number of transport vehicles participating in shared bike dispatching and the actual number of dispatch points as the decision variables, and its optimization goal is to reduce the cost of dispatching shared bikes and improve the utilization rate of shared bikes. The optimization effect of the model before and after the improvement of the genetic algorithm was analyzed, and the proposed optimized Pareto front solution set and optimal scheduling routes for shared bikes were given. Some of the results are noteworthy. First, the factors that affect the scheduling cost and utilization rate of shared bikes vary, so setting the variables reasonably is helpful for improving the optimization ability of the scheduling optimization model. Second, exploring the connections between shared bikes can effectively improve the scheduling efficiency of shared bikes. The research results are of great significance for optimizing dispatching routes and formulating low-carbon shared bike dispatching strategies.

Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies

Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions.

Multi-objective optimal scheduling of an integrated energy system under the multi-time scale ladder-type carbon trading mechanism

Given the ladder-type carbon tax in the carbon quota market is a relatively macroscopic large-time scale concept, and the volatility and uncertainty of new energy generation output and the load, it is currently difficult to accurately predict and control carbon trading cost in the multi-time scale scheduling of integrated energy system (IES), there is a lack of in-depth research on dynamic adjustment and ground implementation in small time scale. To address the challenges of these problems, this paper proposes a multi-objective optimal scheduling model for IES considering the multi-timescale ladder-type carbon trading mechanism. Optimization models and algorithms were developed for day-ahead, intraday and real-time scheduling time scales, enhancing scheduling accuracy and usability. Day-ahead scheduling minimizes comprehensive cost and pollutant emissions while considering the ladder-type carbon trading mechanism; intraday scheduling dynamically adjusts carbon trading cost functions based on day-ahead results and equipment output; and real-time scheduling establishes new carbon trading ladder-type cost functions according to intraday results and emissions. A novel particle swarm optimization algorithm was introduced, integrating uncertainty models for wind, photovoltaic power, and load, along with electric vehicle charging strategies. The multi-time scale model in ladder-type carbon trading exhibits approximately 5% more economic advantage and 2% more environmental benefit.

Low-Carbon Optimal Scheduling Model for Peak Shaving Resources in Multi-Energy Power Systems Considering Large-Scale Access for Electric Vehicles

Aiming at the synergy between a system’s carbon emission reduction demand and the economy of peak shaving operation in the process of optimizing the flexible resource peaking unit portfolio of a multi-energy power system containing large-scale electric vehicles, this paper proposes a low-carbon optimal scheduling model for peak shaving resources in multi-energy power systems considering large-scale access for electric vehicles. Firstly, the charging and discharging characteristics of electric vehicles were studied, and a comprehensive cost model for electric vehicles, heat storage, and hydrogen storage was established. At the same time, the carbon emission characteristics of multi-energy power systems and their emission cost models under specific carbon trading mechanisms were established. Secondly, the change characteristics of the system’s carbon emissions were studied, and a carbon emission cost model of multi-energy power was established considering the carbon emission reduction demand of the system. Then, taking the carbon emission of the system and the peak regulating operation costs of traditional units, energy storage, and new energy unit as optimization objectives, the multi-energy power system peak regulation multi-objective optimization scheduling model was established, and NSGA-II was used to solve the scheduling model. Finally, based on a regional power grid data in Northeast China, the improved IEEE 30 node multi-energy power system peak shaving simulation model was built, and the simulation analysis verified the feasibility of the optimal scheduling model proposed in this paper.

A multi-objective optimization model for the coordinated operation of hydropower and renewable energy

The rapid growth of wind and solar energy sources in recent years has brought challenges to power systems. One challenge is surging wind and solar electric generation, understanding how to consume such generation is important. Achieving the complementarity of hydropower and renewable energies such as wind and solar power by utilizing the flexible regulation performance of hydropower is helpful to provide firm power to help renewable energy consumption. However, the multi-energy complementary operation mode will change the traditional hydropower operation mode, causing challenges to the comprehensive utilization of hydropower. In this paper, a multi-objective optimal scheduling model is built by considering coordinated hydro-wind-solar system peak shaving and downstream navigation. First, the Gaussian mixture model is adopted to quantify the uncertainty of wind and solar power. Then, a hydro-wind-solar coordinated model was built to obtain the standard deviation of the residual load and the standard deviation of the downstream water level. Finally, the ε-constraint method is used to solve for the Pareto optimality. The results demonstrate the following: 1) The proposed model can effectively determine hydropower output schemes that can coordinate wind and solar power output to reconcile peak shaving and navigation; 2) The downstream hydropower stations’ reverse regulation of the upstream hydropower station is a positive factor in reconciling conflicts; and 3) Reasonable planning of wind power and solar power is helpful for hydro-wind solar power complement operation.

Optimal Scheduling of DC Microgrids based on MOPSO Algorithm

The DC microgrid system contains a variety of distributed power sources, but the distributed power sources are uncertain and intermittent. In order to improve the economy of DC microgrid operation, mainly to reduce the microgrid operation cost and environmental management cost, and at the same time the largest proportion of scenery consumption, the optimization algorithm is effective for microgrid for optimal scheduling. A multi-objective optimal scheduling model for microgrids is established. A microgrid model with wind, photovoltaic, micro gas turbine and energy storage is constructed and solved using a multi-objective particle swarm algorithm. The multi-objective particle swarm algorithm is invoked for a typical 24h daily isolated micro-grid system in a certain region. The simulation results show that the optimal dispatching scheme can effectively improve the economy of the microgrid, reduce its operating costs as well as the ability to consume renewable energy scenery power.

Multi-objective Optimal Scheduling of Small Integrated Energy System Based on Improved Particle Swarm Optimization Algorithm

In order to explore a low-carbon and economical way of supplying energy for buildings and promote the transformation of the energy system, a small integrated energy system including energy supply equipment, energy conversion equipment and energy storage equipment is designed. After comprehensively considering operating cost and carbon emission, a multi-objective optimal scheduling model of the system is constructed. A genetic particle swarm optimization algorithm based on random dynamic inertia weight is proposed to solve the model and analyze the output of each piece of equipment in the system under the optimal operating strategy. An example of a building in a region with hot summer and cold winter is introduced for verification, and the simulation results show that the proposed small integrated energy system model is economical and environmentally-friendly, and the improved particle swarm optimization algorithm has a better optimization effect.

Research on user-side flexible load scheduling method based on greedy algorithm

In the context of increasing flexible load represented by smart household appliances, this study aims to establish a demand response model of residential flexible load to minimize electricity cost and reduce grid load variance. In this study, the flexible loads of residents are classified considering different load demand response modes, and demand response models are established for all kinds of loads. To make full use of residential electricity data, this paper established a user side flexible load multi-objective optimization scheduling model, the model for electricity cost and power grid load variance minimizing the objective function, and the safe operation of the meter and the adjustable resources as constraint, load and energy storage battery time-sharing electricity price for resident’s flexible optimization scheduling. Finally, the greedy algorithm is use to calculate and analyze the model. The result show that the model is effective and feasible.

Optimal operation scheduling of household energy hub: A multi-objective optimization model considering integrated demand response

This paper proposes the multi-energy co-optimization of a household energy hub (EH) from the perspective of the supply side. In this paper, firstly, a household load optimization model is established with the minimization problem of the volatility of each load after participating in the integrated demand response. Secondly, to make this study more relevant to reality, an EH multi-objective optimization scheduling model is established in three dimensions: operation cost, carbon emission, and energy efficiency. Finally, a new α-constrained dominated differential evolution algorithm is proposed to obtain the Pareto optimal solution set for EH multi-objective optimization. Then, a compromise optimal day-ahead scheduling scheme is obtained using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. The algorithm used in this paper has better convergence and distributivity than the traditional non-dominant classification genetic algorithm II (NSGA-II) method with constrained internal penalty functions. The results show that users’ cost has dropped by an average of 30%, and energy efficiency has improved by 4.8%. The reduction in run time also allows for handling more significant amounts of load data.

Multi-objective optimal scheduling of reserve capacity of electric vehicles based on user wishes

Due to the considerable number of electric vehicles and the characteristics of energy storage, it is possible for these new energy factors to participate in the operation and regulation of the power system and provide reserve service. In view of this, a multi-objective optimal scheduling model is established, aiming at the economic benefits of electricity collectors, the microgrid power fluctuations, and user satisfaction. Among them, the expression paradigm of user satisfaction is proposed. At the same time, an improved adaptive non-dominated sorting genetic algorithm (NSGA-III-W) was proposed to solve the problem of large-scale and high-dimensional multi-objective in the model. First, an adaptive T-crossover operator is proposed to increase the search and optimization capability of NSGA-III. Second, an adaptive crossover mutation mechanism is proposed to improve the convergence performance of the algorithm. In addition, a compromise solution is selected from the obtained Pareto-dominated solutions through the distance ranking method of superior and inferior solutions (TOPSIS). The improved NSGA-III algorithm, namely the NSGA-III-W algorithm, is compared with the mainstream intelligent optimization algorithms non-dominated sorting genetic algorithm II (NSGA-II) and decomposition-based multi-objective evolutionary algorithm (MOEA\D), and the simulation results demonstrate the feasibility of the proposed model and the effectiveness of the proposed algorithm.

Wind Power Consumption Model Based on the Connection between Mid- and Long-Term Monthly Bidding Power Decomposition and Short-Term Wind-Thermal Power Joint Dispatch

Due to the insufficient consideration of medium and long-term wind power contract power in short-term dispatch, long-term planning and real-time consumption of wind power cannot be effectively undertaken, resulting in a large amount of abandoned wind power. A way to improve the wind power absorption capacity has become an urgent problem to be studied. According to the characteristics of the market and dispatching in the process of wind-fire integration construction, this paper constructs a wind power consumption model that connects the mid- and long-term transaction power decomposition and short-term dispatch. Considering the unit output characteristics and maintenance, the monthly contract electricity is decomposed into daily electricity, and the nesting of medium and long-term transactions and short-term scheduling is realized; the second stage is a short-term multi-objective optimal scheduling model considering the decomposition of contract electricity and the output of non-bidding units to improve the real-time consumption of wind power. Finally, a province in northwest China is taken as an example to verify the effectiveness of the proposed method.

Study on optimal model of micro-energy network operation configuration considering flexible load characteristics

Under the dual pressure of rapidly increasing energy demand and decreasing fossil energy supply and demand and climate change, the pursuit of efficient and clean utilization of energy has become a new trend of energy development. Micro-energy grid is guided by the demand of terminal energy consumption, which can greatly reduce resource waste through coordinated planning and unified scheduling of various types of energy in the network. Firstly, an optimal operation configuration model of micro-energy network considering the characteristics of flexible load is constructed. Secondly, Copula model is used to obtain the joint distribution of wind turbine output and photovoltaic cell output. Optimization objectives including maximizing net operating income and minimizing carbon emissions is also constructed. Thirdly, the gray wolf algorithm is used to solve the multi-objective optimal scheduling model. The results show that: (1) If only the Organic Rankine Cycle (ORC) is operated, the amount of abandoned wind power will increase. After the Power to Gas (P2G) is also operated, it can obviously absorb renewable energy and reduce operating cost. (2) When the micro-energy network is connected to the grid, flexible load can adjust the distribution of load in time dimension.