Optimal Dispatch of the Energy Demand in Electrical Distribution Grid with Reserve Scheduling (original) (raw)
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Optimal Dispatch of Energy/Reserve in Restructured Electricity Market with Demand Variations
The electrical power system plays a crucial role in the development of a country. The power systems are being restructured all over the world to improve efficiency and enhance performance by encouraging competition among various agencies involved in the generation, transmission and distribution of electrical power. Operating reserve plays a significant role in maintaining reliability and security of power supply under constant demand variations. The optimal energy and reserve dispatch routine allocates energy and reserve to different players in such a manner that total cost is minimized and all system constraints are satisfied. In traditional vertically integrated electricity markets reserve is dispatched after completing the energy dispatch but in the competitive market a simultaneous dispatch of energy/reserve is carried out. For this, separate price bids are submitted by power companies for energy and reserve. In this paper a constrained optimization solver ‗fmincon' using the MATLAB optimization tool box is employed for computing the optimal energy/reserve dispatch schedules under dynamic conditions with power balance constraint, generation and reserve min/max capacity constraints, ramp-up/down limits, and energy-reserve-coupling constraints. The proposed algorithm is tested on 17 generating unit, IEEE 57 bus system. Three different test cases are simulated and the proposed method is found to produce feasible results with full satisfaction of complex equality/inequality constraints.
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Earlier the economic dispatch problem was solved for generation first and subsequently for reserve but nowadays in electricity market these two operations are simultaneously performed to increase the efficiency and reliability of the power system. In this study, MATLAB based optimization solver Sequential quadratic programming (SQP) is used to allocate the static and dynamic dispatch in Electricity market based on price bids submitted by the private and public GENCOS. The idea is implemented on IEEE 17-units system with 57 buses for reserve and energy cost optimization under all operating constraints. The realistic representation of the power system is done where constantly changing load demands, generator outages etc. are included in the model. The SQP algorithm is found to model the practical constraints like ramp-limits, reserve/energy coupling constraints, power-balance and generation capacity limits.
IEEE Transactions on Smart Grid, 2015
The distribution network operator is usually responsible for increasing the efficiency and reliability of network operation. The target of active loss minimization is in line with efficiency improvement. However, this approach may not be the best way to decrease the losses payments in an unbundled market environment. This paper investigates the differences between loss minimization and loss payment minimization strategies. It proposes an effective approach for decreasing the losses payment considering the uncertainties of electricity prices in a day ahead energy market using energy storage systems and demand response. In order to quantify the benefits of the proposed method, the evaluation of the proposed technique is carried out by applying it on a 33-bus distribution network.
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Continuous power supply is crucial for any developing economy and its cost efficiency and reliability is highly dependent on operating reserve. Globally, the power systems are adopting a market based structure to enhance performance. In conventional electricity markets reserve gets a second place as its dispatch is performed after energy allocation but in the competitive market these two commodities are dispatched simultaneously to maximize efficiency and reliability. This study proposes a technique based on interior point algorithm for optimizing the dynamic combined energy and reserve dispatch problem with practical complex equality and inequality constraints such as power balance, generation and reserve capacity limits, ramp-up/down limits, and reserve-energy coupling constraints. To simulate practical outage conditions generator contingencies and their effect on operating cost and reserve dispatch is observed. The proposed algorithm is simulated on MATLAB platform and tested for three different cases of the IEEE 57 bus system with 17 generating units. It is found that the proposed method converges to optimal solution for all tested cases and produces feasible solutions where all complex constraints are completely satisfied.
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2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific, 2005
Mathematical optimization provides a formal framework that enables systematic and transparent decision making. Significant progress has been made in recent years in applications of formal optimization techniques for competitive market based resource commitment, scheduling, pricing and dispatch. This paper describes experiences with the application of optimization technology for electricity market operation. Experiences with and the status of the latest development in the security-constrained unit commitment and economic dispatch algorithms are described with references to actual market practices. key words-Electricity market operation, optimal power flow, security-constrained unit commitment and economic dispatch, locational marginal pricing, formal optimization.
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IEEE Access
This paper presents an energy management method for the interconnected operation of power, heat, Combined Heat and Power (CHP) units to settle the Day-Ahead market in the presence of a demand response program (DRP). A major challenge in this regard is the price uncertainty for DRP participants. First, the definitive model of the problem is introduced from the perspective of the Regional Market Manager (RMM) in order to minimize the total supply cost in the presence of TOU program, which is a type of DRP. Furthermore, a market-oriented tensile model is presented in the form of a combination of overlapping generations (OLG) and price elasticity (PE) formulations to determine the amount of electricity demand in the TOU program. Then, a price uncertainty model of the proposed problem is introduced according to the IGDT risk aversion and risk-taking strategies considering information gap decision theory (IGDT). The above problem is solved through the use of the co-evolutionary particle swarm optimization (C-PSO) algorithm and the proposed model is implemented on a standard seven-unit system for a period of 24 hours. INDEX TERMS Combined heat and power, time of use, diamond's OLG model, price uncertainty, information gap decision theory, co-evolutionary particle swarm optimization.
An optimization problem in the electricity market
New types of optimization problems are faced by the generating companies that operate in the Italian electricity market. The characteristics of these problems depend on the various market structures. In the framework of the recently-settled Italian electricity market, one of these new problems is the transition from hourly energy programs, defined by the market, to more detailed power generation dispatches, defined for intervals of fifteen minutes. Such a more detailed plan is needed on the one hand by the national system operator (GRTN, Gestore della Rete di Trasmissione Nazionale) for the assessment of power system stability and security, and on the other by the power plant operators for its implementation. The transition procedure should respect the hourly energy constraints and take into account the main operating constraints of the generating units. The paper presents possible solutions of the problem through linear optimization models and reports computational results on real-world instances.
International Journal of Electrical Power & Energy Systems, 2019
A retailer can sign multiple contracts with participation in demand response program (DRP). The energy sources considered for retailers include pool market and forward contracts. In this paper, several new DRP schemes are proposed for a retailer which is containing pool-order DR, forward DR and rewardbase DR. proposed model is an agreement that retailer will participate it, if is useful. Pool market price uncertainty modeling is one of the main challenges in power system modeling which information gap decision theory (IGDT) is proposed for this uncertainty. In IGDT approach, the robustness and opportunity functions are used to study of different strategies in the presence of pool market price uncertainty. Robustness function is used in the risk-averse strategy while opportunity function is used in the risk-taker strategy. The proposed IGDT risk-constraint strategies of electricity retailer in presence of pool-order DR, forward DR and reward-base DR are modeled via mixed-integer non-linear programming which is solved using SBB solver under GAMS optimization software. To validate the proposed model, two cases are studied and positive effects of proposed DR scheme on the risk-averse, risk-neutral, risk-taker strategies are investigated, and the results are compared with each other.
International Journal of Electrical Power & Energy Systems
Demand response (DR) has recently become an important resource in both system operation and market operation. The focus of this paper is to investigate and quantify the cost impact of various demand response modelings on unit commitment and dispatch in a day-ahead market regime. We have used mixed integer programming unit commitment model, in the market operation framework. Day-ahead market is modeled with a typical test system. Our research results show that DR can exert downward pressure on electricity prices, causing significant implications on social welfare. Results from this work will help policy makers, resource planners, and market designers to make more informed decisions with the goal of better accommodating more demand response resources in the future.
Optimal constrained power scheduling in Electricity market
An optimal scheduling of units in an electric spot market presents in this paper. Unit commitment is a non-linear and complex combinatorial optimization problem which is difficult to be solved for large-scale power systems so this study addresses a linear expression of the problem. Proposed approach is a mixed-integer linear programming to minimize the total energy dispatch cost in 24 hours of a day. A system as the same structure as Iranian power market is used to demonstrate the linear expression of the problem. Simulation results compared with another approach. The results shows the applicability of the proposed method.