Multi-objective Optimal Dispatch Research Articles

Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and economic models. Based on the models and three objectives, the constrained MOO problem is formulated. Then, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. The combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. The final trade-off solutions are decided based on the fuzzy set. The benchmark function tests and simulation results demonstrate that the proposed MOPSO algorithm has better searching ability than nondominated sorting genetic algorithm-II (NSGA-II), which is widely used in generation dispatch for MGs. The proposed method can efficiently offer more Pareto solutions and find a trade-off one to simultaneously achieve three benefits: minimized operation cost, reduced environmental cost, and maximized reliability of service.

Using big data computing framework and parallelized PSO algorithm to construct the reservoir dispatching rule optimization

The paper aims to study how to realize the rational allocation and efficient utilization of water resources among reservoirs and coordinate the balanced optimization of benefit among dispatching objectives under the premise of ensuring flood control safety. A multi-objective optimal dispatching system for reservoirs in Jinsha River basin based on the Spark big data computing framework and parallelized particle swarm optimization (PSO) is proposed. The characteristics of multiple objectives of water resources optimal dispatching system in Jinsha River basin are analyzed. The multiple objectives have been transformed into single objectives, and the solving model of the problem is obtained. Secondly, the parallel algorithm programming model, the PSO algorithm and its parallel strategy for solving optimization problems, and the parallel method of PSO based on Spark big data computing framework are studied. The results show that the research work provides a scientific theoretical basis and a feasible optimization method for the management and dispatch of cascade hydropower stations. Therefore, this study plays a decisive role in promoting the efficient operation of water resources optimal dispatching system and has good reference value for the development and application of big data parallel programming based on Spark platform.

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.

A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters

With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.

Multi-Objective Optimal Dispatch of Microgrid Under Uncertainties via Interval Optimization

This paper presents a model of multi-objective optimal dispatch of microgrid (MODMG) under uncertainties via the interval optimization (IO) approach. In this model, multiple objectives are optimized simultaneously to meet economics, power quality, and security requirements of microgrid (MG) operations. Moreover, in order to adequately consider uncertain power outputs of wind turbines and photovoltaic cells in an MG, they are presented as interval variables. In turn, the MODMG model under uncertainties is formulated as a multi-objective optimization problem via the IO approach, which is then solved by the optimization algorithm of chaotic group search optimizer with multiple producers (CGSOMP). Simulation studies based on a modified IEEE 112-bus MG verify the effectiveness of the proposed optimal dispatch model and the CGSOMP algorithm.

Optimal Economic and Emission Dispatch of a Microgrid with a Combined Heat and Power System

With the rapid development of the new concept of energy internet, electric power systems often need to be investigated together with thermal energy systems. Additionally, to reduce pollution from gas emissions, it is very important to study the economic and emission dispatch of integrated electrical and heating systems. Hence, this paper proposes a multi-objective optimization dispatch model for a microgrid (MG) with a combined heat and power (CHP) system. This CHP-based MG system consists of a CHP unit, a wind turbine, a PV system, a fuel cell, an electric boiler, an electric storage, and a heat storage. It can exchange electricity with the distribution network and exchange heat with the district heating network. Minimum economic cost and minimum environmental cost are considered as the two objectives for the operation of this CHP-based MG system. To solve the two objective optimization problem, the multi-objective bacterial colony chemotaxis algorithm is utilized to obtain the Pareto optimal solution set, and the optimal solution is chosen by the Technique for Order of Preference by Similarity to Ideal Solution method. Finally, numerical case studies demonstrate the effectiveness of proposed model and method for the optimal economic and emission dispatch of the CHP-based MG system.

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 Dispatching of Microgrid With Large-Scale Electric Vehicles

Dispatching the output of distributed power sources is the main task in the microgrid operation phase. This task is more concerned with the optimal dispatch of large electric vehicles connected to the grid-connected microgrid today.Full consider the influence of storage battery and peak-valley electricity price, its objective is to minimize the operating cost of microgrid and the cost of environmental protection, and establishing economic dispatching model of microgrid. To solve this constrained optimization problem, an annealing mutation particle swarm optimization algorithm is proposed. Through simulation and comparison, the dispatching cost results of microgrid are obtained under two dispatching modes of electric vehicle disorder and order. It is concluded that the orderly charging and discharging mode guided by electricity prices can effectively reduce the operating cost and environmental protection cost of microgrid. Improving the economy and reliability of microgrid operation.

An Economic-Reliability Security-Constrained Optimal Dispatch for Microgrids

This paper presents a new security-constrained multiobjective optimal dispatch (SC-MOOD) framework for an economic and reliable operation of microgrids. The framework is developed based on a computationally effective multiobjective optimization technique, Pareto concavity elimination transformation (PaCcET). The new method considers grid steady-state and dynamic constraints while solving the optimal dispatch in both grid-connected and islanded modes. The constraints consist of power balance, voltage magnitude, line flows, power generation, frequency, and voltage transients. The proposed framework finds the most economic operating solutions to not only minimize the generation cost but also minimize the reliability cost. In this paper, the PaCcET is utilized to solve the SC-MOOD. The PaCcET uses an extraordinary transformation to first transfer all the points from a multiobjective space to a transformed objective space. It then solves a linear combination of transformed objectives using a single-objective optimizer to find all the nondominated points of the original multiobjective space. The performance of the new framework is verified using the simulation results in a microgrid with several distributed energy resources in both grid-connected and islanded modes. The results are then compared with two multiobjective optimization techniques, nondominated sorting genetic algorithm II and multiobjective particle swarm optimization, as two well-known benchmarks.

Stochastic multi-objective optimal energy and reactive power dispatch considering cost, loading margin and coordinated reactive power reserve management

In the recent years, modern wind farms (WF) are being increasingly integrated in the power systems. Thanks to the capability curve of doubly-fed induction generator (DFIG) wind turbines, these modern WFs are able to separately supply active and reactive powers. Accordingly, by increasing the penetration of this kind of sources, both voltage stability and reactive power reserve (RPR) management of power system can be affected. This issue makes it important to propose a new methodology for optimal energy and reactive power dispatch in a system with integrated modern WFs in order to effectively take into consideration the capabilities of modern WFs in different aspects of power system operation. Under this perspective, this paper proposes a stochastic multi-objective optimal dispatch (SMO-OD) problem in the presence of modern WFs to make a techno-economic framework for the system operators (SOs). The proposed SMO-OD problem aims at minimizing expected payments of energy and reactive power markets, and maximizing expected RPR and loading margin (LM) in the presence of wind power generation uncertainty. This paper also contributes to the relevant literature by formulating a model which determines the RPR of modern WFs to be included in the management of total RPR of the power system. Also, a coordinated RPR (CRPR) scheme is proposed by using this model. The proposed model benefits from a detailed reactive power output model of the DFIG wind turbines in order to handle real-world power system operation. Two studies are considered in the proposed SMO-OD problem: (i) SMO-OD with uncoordinated RPR provision (SMO-OD-URPR), and (ii) SMO-OD with the CRPR provision (SMO-OD-CRPR). The proposed SMO-OD problem is applied to the IEEE 30-bus test system. Simulation results show that using the proposed model, a proper trade-off is made between technical and economic aspects of power system operation, and considerable LM and RPR are provided. In the proposed SMO-OD-CRPR problem, the value of expected RPR increases compared to SMO-OD-URPR problem, and the DFIG-based WF has more expected RPR to inject to grid under contingency or sudden increased load. Moreover, by increasing the wind penetration level, not only does the value of expected payments of energy and reactive power markets reduce, but also the value of expected LM and RPR increases.

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.

Analysis of Multi-objective Optimal Dispatch of Cogeneration with Thermal Energy Storage for Building Energy Management System

Analysis of Multi-objective Optimal Dispatch of Cogeneration with Thermal Energy Storage for Building Energy Management System

Multi-objective optimal dispatch of microgrid containing electric vehicles

As an important part of smart grid optimizations, optimal dispatch of microgrid is of great significance to reduce energy consumption, environmental pollution and user's electricity costs. The development objective of microgrid is not only to meet the basic demand of power supply, but also to improve its economic efficiency and environmental protection. In this regard, this paper proposes a multi-objective optimal dispatch model for microgrid under grid-connected mode, which comprehensively considers the operation cost and the environmental protective cost of the microgrid system. Distribution generators in the microgrid system include photovoltaic array, wind turbine, diesel engine, micro turbine and electric vehicle. The batteries of the accessed electric vehicles are treated as a kind of mobile distributed energy storage device in the model. In order to solve the proposed model effectively, according to the configuration and characteristics of the model, this paper proposes an improved particle swarm optimization algorithm to solve the optimization model. Two different operation scheduling scenarios under grid-connected mode are researched, and the dispatch results of different optimization goals are discussed in this paper. The simulation results show that the effectiveness and superiority of the proposed model, which can effectively reduce the user's electricity costs and environmental pollution, promote the optimal operation of microgrid and achieve the economic management of the batteries of electric vehicles.

An overview and comparative analysis of recent bio-inspired optimization techniques for wind integrated multi-objective power dispatch

Abstract Over the last few decades, bio-inspired (BI) evolutionary optimization techniques have experienced overwhelming popularity, extraordinary growth and large number of applications, particularly, in the field of engineering and technology. These techniques present a tough competition to traditional numerical methods which suffer from convexity and continuity assumptions and which normally employ a gradient based search that is sensitive to the initial solution. While initial BI techniques suffered from limitations such as premature convergence and dependence on control parameters, eventually, these issues were specifically addressed by improved variants and many novel BI methods. The population based computing methods are particularly attractive for solving multi-objective (MO) problems due to their capability of producing a large number of Pareto-optimal solutions in one run. In this paper, an integrated ranking index (IRI) composed of TOPSIS and fuzzy-min concept is proposed as a performance metrics to aggregate the different objectives. The performance of eight handpicked recent BI techniques is compared for the solution of wind integrated multi-objective optimal power dispatch (MOOD) problem for simultaneous minimization of fuel cost and emission. Due to the uncertain nature of wind power (WP), the effect of its over and underestimation on both economic as well as environmental aspects, has also been considered. Six standard test cases having non-convex, multi-modal and discontinuous objective functions, dynamic operation and complex equality/inequality constraints, are selected for testing Flower Pollination Algorithm (FPA), Mine Blast Algorithm (MBA), Backtracking Search Algorithm (BSA), Symbiotic Organisms Search (SOS), Ant Lion Optimizer (ALO), Moth-Flame Optimization (MFO), Stochastic Fractal Search (SFS) and Lightning Search Algorithm (LSA).

Multi-objective Optimal Dispatch in Power System Based On a Modified Multi-Objective Teaching-Learning Algorithm

Multi-objective Optimal Dispatch in Power System Based On a Modified Multi-Objective Teaching-Learning Algorithm

Normalised normal constraint algorithm applied to multi‐objective security‐constrained optimal generation dispatch of large‐scale power systems with wind farms and pumped‐storage hydroelectric stations

A multi-objective security-constrained optimal generation dispatch (MOSCOGD) model for large-scale power systems with wind farms and pumped-storage hydroelectric (PSH) stations is proposed. In this model, fuel consumption, emission content of atmospheric pollutants, and power purchase costs are taken into account as objective functions, and network security under basic status and N − 1 criterion conditions, and the operational limits of PSH resources are included in the constraints. The difficulties associated with discrete variables for describing the operational characteristics of PSH resources are avoided by introducing two continuous variables, generation power and pumping power, which satisfy complementary constraints. The normalised normal constraint algorithm is used to transform the three-objective optimal dispatch model into a series of single-objective optimisation models, which are solved by the interior point method, resulting in a series of evenly distributed Pareto optimal solutions (POSs) and the complete Pareto frontier surface. The computational efficiency is greatly enhanced by employing a checking–adding–checking again-adding again scheme to address network security constraints. Moreover, parallel computing is employed to improve the computation speed for solving the POSs of the MOSCOGD model. Test results on an actual large-scale power system and the modified IEEE 39-bus system demonstrate the effectiveness of the proposed method.

不同类型水电站群优化调度模型的大系统分解协调法研究

根据串并联水电站、混联水电站等不同类型水电站群的特点，建立了以发电量最大为目标的优化调度模型，模型同时考虑了水电站群间水力联系、电力联系等影响因素。针对不同类型水电站群优化调度模型构建了大系统分解协调算法，分别对模型问题分解、协调算法和收敛条件进行了详细的说明与推导。以清江流域水布垭、隔河岩、高坝洲水电站为例，建立了考虑梯级间流量传播影响和电力联系的3级串联水电站优化调度的大系统分解协调模型，大系统分解协调算法较传统算法计算效率更高，可以为大流域跨省区的复杂并联、混联水电站群多目标优化调度模型的构建、问题分解、以及算法等方面提供参考。 According to the characteristics of different types of hydropower stations such as cascade and parallel hydropower stations, and mixed hydropower stations, the optimal dispatching model with the largest generating capacity was established. The model considers the influencing factors such as hydraulic connection and power connection between hydropower stations. A large system decomposition and coordination algorithm was used for the optimal scheduling model of different types of hydropower stations. The problem decomposition, coordination algorithm and convergence conditions were described and deduced respectively. A large-scale system decomposition and coordination model of three-stage cascade hydropower station considering the influence of the flow rate between the steps and the power connection was established, taking the Shuibuya, Geheyan and Gaobazhou hydropower stations in the Qingjiang River basin as an example. The proposed method is more efficient than the traditional algorithm, which can provide reference for the complex parallelization of the inter-provincial provinces in the large watershed, the construction of the multi-objective optimal dispatching model of the hybrid hydropower station group, the problem decomposition, and the algorithm.

Optimal allocation of SVC and TCSC using quasi-oppositional chemical reaction optimization for solving multi-objective ORPD problem

This paper presents an efficient quasi-oppositional chemical reaction optimization (QOCRO) technique to find the feasible optimal solution of the multi objective optimal reactive power dispatch (RPD) problem with flexible AC transmission system (FACTS) device. The quasi-oppositional based learning (QOBL) is incorporated in conventional chemical reaction optimization (CRO), to improve the solution quality and the convergence speed. To check the superiority of the proposed method, it is applied on IEEE 14-bus and 30-bus systems and the simulation results of the proposed approach are compared to those reported in the literature. The computational results reveal that the proposed algorithm has excellent convergence characteristics and is superior to other multi objective optimization algorithms.