Economic dispatch problem in smart grid system with considerations for pumped storage (original) (raw)
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Lecture Notes in Computer Science, 2010
The implementation of intelligent power grids, in form of smart grids, introduces new challenges to the optimal dispatch of power. Thus, optimization problems need to be solved that become more and more complex in terms of multiple objectives and an increasing number of control parameters. In this paper, a simulation based optimization approach is introduced that uses metaheuristic algorithms for minimizing several objective functions according to operational constraints of the electric power system. The main idea is the application of simulation for computing the fitness-values subject to the solution generated by a metaheuristic optimization algorithm. Concerning the satisfaction of constraints, the central concept is the use of a penalty function as a measure of violation of constraints, which is added to the cost function and thus minimized simultaneously. The corresponding optimization problem is specified with respect to the emerging requirements of future smart electric grids.
Comparative study of metaheuristics methods applied to smart grid network in Morocco
International Journal of Power Electronics and Drive System (IJPEDS) , 2020
The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. The unit commitment problem (UCP) consists mainly in finding the minimum cost schedule for a set of generators by switching on or off each one over a given time horizon to meet the demand and satisfy different operational constraints, This research article integrates the crow search algorithm (CSA) as a local optimizer of Eagle strategy (ES) to solve unit commitment problem in smart grid system and economic dispatch of two electricity networks: a testing system 7 units and the Moroccan network.. The results obtained by ES-CSA are compared with various results obtained in the literature. Simulation results show that using ES-CSA can lead to finding stable and adequate power generated that can fulfill the need of both the civil and industrial areas.
Economic dispatch problem (EDP) is an important class of optimization problems in the smart grid, which aims at minimizing the total cost when generating certain amount of power. In this work, a novel consensus based algorithm is proposed to solve EDP in a distributed fashion. The quadratic convex cost functions are assumed in the problem formulation, and the strongly connected communication topology is sufficient for the information exchange. Unlike centralized approaches, the proposed algorithm enables generators to collectively learn the mismatch between demand and total amount of power generation. The estimated mismatch is then used as a feedback mechanism to adjust current power generation by each generator. With a tactical initial setup, eventually, all generators can automatically minimize the total cost in a collective sense.
Economic Dispatch of Distributed Generation Using Backtracking Search Optimization Algorithm
2016
Nowadays, distributed generators (DG) are most widely used in distribution system to satisfy the increasing demand. According to the demand, the dispatch of generator should be modified for economic operation. The economic dispatch (ED) of DGs are normally solved by anyone of the following methods: conventional methods such as Lambda iteration method, Dynamic Programming etc., or optimization technique such as Genetic algorithm (GA), Evolutionary Programming (EP), Differential Evolution (DE) Algorithm etc., These methods of solving ED problem require comparatively large computation time. Therefore, it is important to estimate real power dispatch values within a short period. This paper presents the ED of various DGs for different demands using Backtracking Search Optimization Algorithm (BSA). In this work two diesel engines, single units of wind turbine generator and fuel cell are used as DG. The ED problem is solved for IEEE 33 bus distribution system by BSA and DE. The test result shows that the BSA method is better for ED of DG than DE.
EAI Endorsed Trans. Energy Web, 2021
An ideal scheduling model of Thermal-Wind and Pumped Storage Hydro framework is introduced in this paper. Thermal-WindPumped store up Generation Scheduling (TWPGS) with practical and natural highlights of a multi-target optimization issue with many figured constraints are proposed. The fundamental target is to create 60 minutes by-hour ideal schedule of Thermal-windPumped Storage plants to lessen the operating cost of the thermal power plant and fulfilling the system constraints. The paper introduces a point by point structure of the TWPGS issue, and Gravitational Search Algorithm (GSA) is proposed to formulate the solution. The proposed GSA chooses the ON/OFF situation of the generating unit and resultant power dispatch with least operating expense. To decide the effectiveness of the GSA model, testing is executed in a standard IEEE 6 bus framework utilizing MATLAB programming. The ideal consequences of the proposed model have been confirmed with the current method. The examination...
Solving the Economic Load Dispatch Problem Using Crow Search Algorithm
2017
Economic Load Dispatch (ELD) problem concern on scheduling the committed generating units outputs such that the load in demand can be provided with minimum operating cost while satisfying all units and system equality and inequality constraints. This paper proposes the use of Crow Search Algorithm (CSA) to solve the ELD Problem. CSA is yet another metaheuristic search algorithm that adopt the method of crows when they search, hide and retrieve food when needed. CSA is explored to solve the nonlinear ELD constrained optimization problem for a three units power system. The results obtained by CSA are compared with various results obtained in the literature. Simulation results shows that using CSA can lead to finding stable and adequate power generated that can fulfill the need of both the civil and industrial areas.
Dynamic economic dispatch using complementary quadratic programming
Energy, 2019
Economic dispatch for micro-grids and district energy systems presents a highly constrained non-linear, mixed-integer optimization problem that scales exponentially with the number of systems. Energy storage technologies compound the mixed-integer or unit-commitment problem by necessitating simultaneous optimization over the applicable time horizon of the energy storage. The dispatch problem must be solved repeatedly and reliably to effectively minimize costs in real-world operation. This paper outlines a methodology that greatly reduces, and under some conditions eliminates, the mixed-integer aspect of the problem using complementary convex quadratic optimizations. The generalized method applies to grid-connected or islanded district energy systems comprised of any variety of electric or combined heat and power generators, electric chillers, heaters, and all varieties of energy storage systems. It incorporates constraints for generator operating bounds, ramping limitations, and energy storage inefficiencies. An open-source platform, EAGERS, implements and investigates this optimization method. Results demonstrate the efficacy of the optimization method benchmarked against a commercial mixed-integer solver. Index Terms-Economic dispatch, energy storage, quadratic programming, unit commitment, mixed-integer relaxation as electric vehicle charging, intermittent renewable power generation, energy storage, and participation in ancillary grid service markets further complicate the energy management problem. Energy storage, in particular, offers tremendous potential to improve energy management in district energy systems. Energy storage systems are designed and utilized for one or more purposes; load smoothing, peak shaving, and energy arbitrage. Small capacity energy storage, storing less than 1% of daily use, is typically applied to 'load smoothing': balancing short-term disparities between demand and generation arising due to limitations in generator responsiveness. Intermediate scale energy storage, storing less than 5% of daily use, often provides 'peak shaving': avoiding short term power surges which incur peak demand tariffs ($/kW) by augmenting generation during peak load events. Larger storage systems, storing more than 5% of daily use, provides arbitrage, i.e. shifting consumption from expensive periods to inexpensive periods. Management to accomplish all three is a challenging control problem closely tied to the scheduling of all other distributed energy assets. Management techniques must repetedly and reliably dispatch all systems in a timely fashion in order to minimize operating costs and maximize the utility of storage assets. Economic dispatch is a centralized approach to determining the optimal scheduling of generators, known as unit commitment. This paper focuses on efficiently and reliably solving the centralized economic dispatch problem, but should be seen as complementary to other methods of microgrid dispatch. Multi-agent control is a promising decentralized control method [3,4,5] with advantages when developing 'plug-and-play' generators. With a pre-defined communication system, generators can be readily integrated into a bidding process which allocates generation duties between network systems using market based strategies. The drawback to decentralized control is the loss of capability that forecasting provides, which becomes paramount in the presence of energy storage. Centralized control has the potential to see the bigger picture and dispatch generators according to present demands, while anticipating future demands. However, the informational requirements of centralized control can be substantial [6]. The information requirments include the costs, capacity, performance and response capabilities of each distributed energy resource (DER), the time-of-day and/or weather dependence of the load, and the grid costs and interconnection constraints. Centralized controllers may also include market type bidding strategies when selling capacity or ancillary services to the grid [1,7]. Methods for determining economic dispatch include a number of heuristic [8] or metaheuristic search methods including genetic algorithms [9,10], mixed integer linear programming [10], dynamic programming [11], and simulated annealing methods [12]. Search methods have been inspired by metal processing (i.e. simulated annealing)
Imperialist Competitive Algorithm for Dynamic Optimization of Economic Dispatch in Power Systems
Lecture Notes in Computer Science, 2012
As energy costs are expected to keep rising in the coming years, mostly due to a growing worldwide demand, optimizing power generation is of crucial importance for utilities. Economic power dispatch is a tool commonly used by electric power plant operators to optimize the use of generation units. Optimization algorithms are at the center of such techniques and several different types of algorithms, such as genetic or particle swarm algorithms, have been proposed in the literature. This paper proposes the use of a new metaheuristic called imperialist competitive algorithm (ICA) for solving the economic dispatch problem. The algorithm performance is compared with the ones of other common algorithms. The accuracy and speed of the algorithm are especially studied. Results are obtained through several simulations on power plants and microgrids in which variable numbers of generators, storage units, loads and grid import/export lines are connected.
Optimal dispatch of Renewable Energy Sources in smart grid pertinent to virtual power plant
2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), 2013
In order to alleviate the effects of greenhouse gas emissions, the environmental and economic dispatch (EED) is formulated as multiobjective optimization problem (MOP) solved by multiobjective immune algorithm (MOIA). Building on this model, the virtual power plant (VPP) is proposed involving distributed generation (DG), interruptible load (IL), and energy storage (ES) to participate in joint energy and reserve markets. The uncertainties of load prediction, DG, and IL are treated as an interval-based optimization in this study. The static and real-time simulations are conducted to demonstrate the validity of proposed stochastic EED model through the IEEE 30-bus test system.
IJERT-Advanced intelligent dispatch control in power generation
International Journal of Engineering Research and Technology (IJERT), 2012
https://www.ijert.org/advanced-intelligent-dispatch-control-in-power-generation https://www.ijert.org/research/advanced-intelligent-dispatch-control-in-power-generation-IJERTV1IS10033.pdf With the fast development of technologies of alternative energy, the electric power network can be composed of several renewable energy resources. Time of use (TOU) pricing creates more energy-efficient and renewable-energy-friendly grid. It is possible to better utilize the energy production by incorporating an energy storage device (ESD). GA algorithm is used for creating dispatching schedules for customer-owned renewable energy systems coupled with energy storage. Genetic algorithms are adaptive search methods that simulate some of the natural processes: selection, information, inheritance, random mutation and population dynamics. Adding energy storage along with this algorithm to renewable generators would increase the rate of return for the owner of the system and help the utility to reduce peak load. In the next step PSO algorithm can be used and will compare the results. Index Terms-Distributed generation, energy storage device, genetic algorithm, renewable energy, smart grid, time of day pricing.