Solution of Different Types of Economic Load Dispatch Problems Using a Pattern Search Method

Direct search (DS) methods are evolutionary algorithms used to solve constrained optimization problems. DS methods do not require information about the gradient of the objective function when searching for an optimum solution. One such method is a pattern search (PS) algorithm. This study presents a new approach based on a constrained PS algorithm to solve various types of power system economic load dispatch (ELD) problems. These problems include economic dispatch with valve point (EDVP) effects, multi-area economic load dispatch (MAED), companied economic-environmental dispatch (CEED), and cubic cost function economic dispatch (QCFED). For illustrative purposes, the proposed PS technique has been applied to each of the above dispatch problems to validate its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method has been assessed and investigated through comparison with results reported in literature. The outcome is very encouraging and suggests that PS methods may be very efficient when solving power system ELD problems

Optimal economic and emission dispatch of photovoltaic integrated power system using firefly algorithm

The main purpose of Economic Load Dispatch (ELD) is to determine the optimal output of generating units to meet the power demand at lowest possible cost and subjected to the operational constraints. Various ELD optimization methods have been developed in order to deal with the challenges of continuous and sustainable power at optimal cost. The deficiency of fossil fuel reserve and rapid increase of fuel prices to generate electricity has encouraged the use of Renewable Energy (RE). Furthermore, concerns over environmental pollution also become a factor to incorporate the RE and fossil fuel in generating electricity. This project propose the Firefly Algorithm (FA) to solve Economic and Emission Load Dispatch (EELD) problems that consists of photovoltaic systems. The FA algorithm is used to determine the optimal cost and emission levels of power generation. The test case considered in this project is Static Combined Economic and Emission Dispatch (SCEED) that been simulated for each hour. The test system with 6 units of thermal generator and 13 units of PV generator are used to optimize SCEED problem by using FA. The Weight Sum Method (WSM) approach is used to determine the best compromise solution among the cost and emission. It found that FA can provide the fast convergence in finding the global minima value. It can be concluded that FA can solve the problem of economic and emission dispatch accurately

An Online Decision-Theoretic Pipeline for Responder Dispatch

The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.Comment: Appeared in ICCPS 201

Variable Weighted Multi-Objective Multi-Dimensional Genetic Algorithm for Demand Response Scheduling in a Smart Grid

This research presents the optimized scheduling of demand response loads of a residential community of 30 houses using a multi-objective multi-dimensional genetic algorithm (MOMD-GA) with a variable weighted objective function. Incorporating day ahead hourly real time pricing (RTP), the MOMD-GA attempts to present possible optimized dispatch patterns with their associated penalties and constraints (environmental, consumers and suppliers) thus providing system operators (SOs) and distribution network operators (DNOs) sufficient data for real time decision making. The variable weights for each considered component of the cost function is chosen to force the MOMD-GA towards exploring optimum solutions with lower environmental cost. Further shown are the trade-offs in selecting particular dispatch bias (consumer, supplier, environmental and optimized) and the impact of the various dispatch scenarios on the cost of overall electricity bill of the community

Economic dispatch model considering policy-guided carbon trading mechanisms

PowerTech is the anchor conference of the IEEE Power & Energy Society in EuropeClimate change has become one of the most serious problems nowadays. Electric power industry, as a major emitter of greenhouse gas CO2, has been demanded to develop in an environmental friendly way. This paper presents economic dispatch model considering carbon reduction policies, as well as basic data analysis to determine the impact of policy-guided carbon emission reduction strategies on power system operation and dispatch. Carbon reduction mechanisms are simulated and discussed in different case studies. By analyzing the effect of different carbon reduction policies, this model can be used to help regulators with decision making and system operators with economical and environmental operation. © 2013 IEEE.published_or_final_versio

Comparing post-combustion CO2 capture operation at retrofitted coal-fired power plants in the Texas and Great Britain electric grids

Stuart Cohen is with UT Austin, Hannah Chalmers is with University of Edinburgh, Michael Webber is with UT Austin, and Carey King is with UT AustinThis work analyses the carbon dioxide (CO2) capture system operation within the Electric Reliability Council of Texas (ERCOT) and Great Britain (GB) electric grids using a previously developed first-order hourly electricity dispatch and pricing model. The grids are compared in their 2006 configuration with the addition of coal-based CO2 capture retrofits and emissions penalties from 0 to 100 US dollars per metric ton of CO2 (USD/tCO2). CO2 capture flexibility is investigated by comparing inflexible CO2 capture systems to flexible ones that can choose between full- and zero-load CO2 capture depending on which operating mode has lower costs or higher profits. Comparing these two grids is interesting because they have similar installed capacity and peak demand, and both are isolated electricity systems with competitive wholesale electricity markets. However, differences in capacity mix, demand patterns, and fuel markets produce diverging behaviours of CO2 capture at coal-fired power plants. Coal-fired facilities are primarily base load in ERCOT for a large range of CO2 prices but are comparably later in the dispatch order in GB and consequently often supply intermediate load. As a result, the ability to capture CO2 is more important for ensuring dispatch of coal-fired facilities in GB than in ERCOT when CO2 prices are high. In GB, higher overall coal prices mean that CO2 prices must be slightly higher than in ERCOT before the emissions savings of CO2 capture offset capture energy costs. However, once CO2 capture is economical, operating CO2 capture on half the coal fleet in each grid achieves greater emissions reductions in GB because the total coal-based capacity is 6 GW greater than in ERCOT. The market characteristics studied suggest greater opportunity for flexible CO2 capture to improve operating profits in ERCOT, but profit improvements can be offset by a flexibility cost penalty.Mechanical Engineerin

A genetic algorithm based economic dispatch (GAED) with environmental constraint optimisation

The role of renewable energy in power systems is becoming more significant due to the increasing cost of fossil fuels and climate change concerns. However, the inclusion of Renewable Energy Generators (REG), such as wind power, has created additional problems for power system operators due to the variability and lower predictability of output of most REGs, with the Economic Dispatch (ED) problem being particularly difficult to resolve. In previous papers we had reported on the inclusion of wind power in the ED calculations. The simulation had been performed using a system model with wind power as an intermittent source, and the results of the simulation have been compared to that of the Direct Search Method (DSM) for similar cases. In this paper we report on our continuing investigations into using Genetic Algorithms (GA) for ED for an independent power system with a significant amount of wind energy in its generator portfolio. The results demonstrate, in line with previous reports in the literature, the effectiveness of GA when measured against a benchmark technique such as DSM

Modelling of firm offer from combined wind and hydro generations

This paper analyses the impact of a firm combined offer by wind and small hydro generators located in the river chain, with a view to address the intermittency of wind generators. Both generations are dispatchable and cleared against their offer prices. They offer a firm, hourly-schedule (WH schedule) for 24 hours ahead of real-time operation to an auction based locational marginal price (LMP) market with other generators offering to meet the system loads. The model network consists of other generators and loads at different buses. The scheduled power is taken off at a predetermined bus, as a load at the bus. This schedule must be met by the wind and hydro combined generations. If necessary, a notional thermal generation is available at a considerable higher price to meet the schedule, at the offtake point. The objective is to minimise the total supply cost for 24 hours and examine LMPs and constraint-on costs while respecting the given WH schedule, nodal power balance constraint, generation limits, branch flow and other limits. Discussion is based on New Zealand (NZ) Electricity Market rules, where generators are self-committed into the market. Three scenarios are studied and the results presented