Joint energy and reserve dispatch in a competitive pool usingLagrangian relaxation

A Lagrangian relaxation solution method is applied by the authors to the problem of joint energy and reserve dispatch in a pool-oriented electricity market     

Optimal dispatch strategy in remote hybrid power systems

For small villages in developing countries, local stand-alone power systems are often more cost-effective than utility grid extension. Various combinations of wind turbine generators, photovoltaic arrays, diesel gensets, and batteries—remote hybrid power systems—may be preferred to diesel-only systems. Dispatch strategy is the aspect of control strategy that pertains to energy flows among components. In systems with both batteries and diesel genset(s), dispatch affects the life-cycle cost through both the fuel usage and the battery life. In this study, dispatch strategies are compared using (1) an analysis of cost trade-offs, (2) a simple, quasi-steady-state time-series model, and finally (3) HYBRID2, a more sophisticated stochastic time-series model. An idealized predictive dispatch strategy, based on assumed perfect knowledge of future load and wind conditions, is developed and used as a benchmark in evaluating simple, non-predictive strategies. The results illustrate the nature of the optimal strategy and indicate that one of two simple diesel dispatch strategies—either load-following or full power for a minimum run time—can, in conjunction with the frugal use of stored energy (the Frugal Discharge Strategy), be virtually as cost-effective as the Ideal Predictive Strategy. The optimal choice of these two simple charging strategies is correlated to three dimensionless parameters, yielding a generalized dispatch design chart for an important class of systems.     

Power economic dispatch using a hybrid genetic algorithm

This letter outlines a hybrid genetic algorithm (HGA) for solving the economic dispatch problem. The algorithm incorporates the solution produced by an improved Hopfield neural network (NN) as a part of its initial population. Elitism, arithmetic crossover and mutation are used in the GAs to generate successive sets of possible operating policies. The technique improves the quality of the solution and reduces the computation time, and is compared with the classical optimization technique, an improved Hopfield NN approach (IHN), a fuzzy logic controlled GA and an improved GA     

Optimal sales-mix and generation plan in a two-stage electricity market

A bi-level stochastic programming problem is used to model the optimal decision of a risk averse electricity producer, interacting in a two-stage market with cost minimizer competitors. His decision variables include the distribution of production (which plant of different technologies and variable costs to operate) and the sales-mix (how much generation to commit to bilateral contracts and spot market). To enhance computation times, the bi-level problem is transformed into a Mixed-Integer Linear Problem (MILP) by applying sophisticated linearization techniques. Electricity demand, Renewable Energy Sources (RES) generation and production costs are different sources of uncertainty. A copula method is used to generate scenarios under different correlations values (between RES generation and demand), to analyze the impact of correlation on the optimal solution. The model is tested through extensive numerical simulations based on data from the Spanish electricity market. The results show that correlation and risk aversion have a relevant impact on how sales-mix and generation plan decisions should combine optimally.     

Distributed generation and demand response dispatch for a virtual power player energy and reserve provision

Recent changes in the operation and planning of power systems have been motivated by the introduction of Distributed Generation (DG) and Demand Response (DR) in the competitive electricity markets' environment, with deep concerns at the efficiency level. In this context, grid operators, market operators, utilities and consumers must adopt strategies and methods to take full advantage of demand response and distributed generation. This requires that all the involved players consider all the market opportunities, as the case of energy and reserve components of electricity markets.The present paper proposes a methodology which considers the joint dispatch of demand response and distributed generation in the context of a distribution network operated by a virtual power player. The resources' participation can be performed in both energy and reserve contexts. This methodology contemplates the probability of actually using the reserve and the distribution network constraints. Its application is illustrated in this paper using a 32-bus distribution network with 66 DG units and 218 consumers classified into 6 types of consumers.     

Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm

This paper proposes the generation scheduling approach for a microgrid comprised of conventional generators, wind energy generators, solar photovoltaic (PV) systems, battery storage, and electric vehicles. The electrical vehicles (EVs) play two different roles: as load demands during charging, and as storage units to supply energy to remaining load demands in the MG when they are plugged into the microgrid (MG). Wind and solar PV powers are intermittent in nature; hence by including the battery storage and EVs, the MG becomes more stable. Here, the total cost objective is minimized considering the cost of conventional generators, wind generators, solar PV systems and EVs. The proposed optimal scheduling problem is solved using the hybrid differential evolution and harmony search (hybrid DE-HS) algorithm including the wind energy generators and solar PV system along with the battery storage and EVs. Moreover, it requires the least investment.     

Energy and ancillary service dispatch in a competitive pool

Competitive bidding in a pool electricity market is likely to extend beyond energy to include ancillary services. The purpose of this paper is to identify and analyze alternative methods for dispatching ancillary services in a pool-oriented electricity market     

Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using the hybrid bacterial foraging-Nelder-Mead algorithm

In this paper, a new approach is proposed to solve the economic load dispatch (ELD) problem. Power generation, spinning reserve and emission costs are simultaneously considered in the objective function of the proposed ELD problem. In this condition, if the valve-point effects of thermal units are considered in the proposed emission, reserve and economic load dispatch (ERELD) problem, a non-smooth and non-convex cost function will be obtained. Frequency deviation, minimum frequency limits and other practical constraints are also considered in this problem. For this purpose, ramp rate limit, transmission line losses, maximum emission limit for specific power plants or total power system, prohibited operating zones and frequency constraints are considered in the optimization problem. A hybrid method that combines the bacterial foraging (BF) algorithm with the Nelder-Mead (NM) method (called BF-NM algorithm) is used to solve the problem. In this study, the performance of the proposed BF-NM algorithm is compared with the performance of other classic (non-linear programming) and intelligent algorithms such as particle swarm optimization (PSO) as well as genetic algorithm (GA), differential evolution (DE) and BF algorithms. The simulation results show the advantages of the proposed method for reducing the total cost of the system.     

Optimal economic study of hybrid PV-wind-fuel cell system integrated to unreliable electric utility using hybrid search optimization technique

This study addresses the problem of power outages in distant districts by taking advantage of the available renewable energy resources in the surrounding environment. This was done by proposing connecting the utility to a hybrid system constituting from photovoltaic (PV), wind turbine (WT), and fuel cell (FC) systems where this hybrid system is considered as a backup system that works when the grid is unavailable. This hybrid system proposed is used for feeding the load to a tourist resort in Hurghada, Egypt.The design of the introduced system has taken into consideration the cost of purchasing electric energy and the profit from selling it to the utility network. Component scaling was implemented to improve the net present cost of the proposed system using two grouped meta-heuristic techniques, which are the Hybrid Firefly and Harmony Search optimization technique (HFA/HS) and compared to the particle swarm optimization (PSO) technique.Simulation results have shown that the optimal system for solving the grid unavailability consists of eighty PVs, two WTs, twenty FCs, forty-one electrolyzers, and one hundred eighteen hydrogen tanks. The results also showed that the volume of exchange with the grid has reached 4 GW of purchase and 3 GW of sale. It is manifest from the results that the suggested system is economically viable with an LCOE of 0.0628 $/kWh, which is less than the purchase of electricity from the grid for commercial users in Egypt, which is 0.1 $/kWh.     

Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design

This paper presents a probabilistic multiobjective framework for optimal distributed energy resources (DERs) planning in the distribution electricity networks. The proposed model is from the distribution company (DISCO) viewpoint. The projected formulation is based on nonlinear programming (NLP) computation. The proposed design attempts to achieve a trade-off between minimizing the monetary cost and minimizing the emission of pollutants in presence of the electrical load as well as electricity market prices uncertainties. The monetary cost objective function consists of distributed generation (DG) investment and operation cost, payment toward loss compensation as well as payment for purchased power from the network. A hybrid fuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used for scenario based modeling of the electricity prices and a combined roulette-wheel/Monte-Carlo simulation (RW/MCS) model is used for generation of the load scenarios. The proposed planning model considers six different types of DERs including wind turbine, photovoltaic, fuel cell, micro turbine, gas turbine and diesel engine. In order to demonstrate the performance of the proposed methodology, it is applied to a primary distribution network and using a fuzzified decision making approach, the best compromised solution among the Pareto optimal solutions is found.     

Optimal bidding strategy in a competitive electricity market based on agent-based approach and numerical sensitivity analysis

The objective of this study is to present a new method for determination of the optimal bidding strategies among generating companies (GenCo) in the electricity markets using agent-based approach and numerical sensitivity analysis (NSA). While agent-based approach provides for decision making, NSA can help with identifying the critical control points that lead to proper decisions to be taken by GenCos. To achieve the objective, the pricing mechanism used for settling the electricity market and determining the GenCos rewards is locational marginal pricing (LMP) and the sensitivity of each GenCo reward with respect to its bid is analyzed, then, the optimal strategy is determined. An example and a case study are used to illustrate the efficiency of the proposed method. The LMPs and allocated generations of GenCos show that the proposed method leads GenCos to learn a strategic manner and, as a result, increase prices and maximize their rewards. To validate the proposed method, the results from this study are compared with those available in the literature. The comparison of results shows an improved simulation time by 8.16 percent and total reward of market by 2.46 percent.     

Acquiring energy resources in a competitive market

Since the opening of the energy market to competitive forces, power delivery companies have seen increased uncertainty regarding the source of energy to serve customers in their service areas. For many companies, the decisions related to the siting, timing, and operation of individual totally out of their control. These companies and their regulators are learning to cope with the new skills and techniques necessary to assure the availability of supply in this new market. The problem is made more difficult for many because they are prohibited from owning and operating generation resources due to fear that they will exercise vertical market power and prevent consumers from gaining access to competitively priced energy. When the series of discussions that formed the basis for this article began, the question to be answered was: How do energy delivery and energy service companies plan for the construction of new generating plants in a deregulated environment? The answer is, they don't. They must team a new set of skills to match a set of contracts, options, and other derivatives to the forecast for energy. They must manage risk using market instruments.     

Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search

In this paper, an optimization method for the reactive power dispatch in wind farms (WF) is presented. Particle swarm optimization (PSO), combined with a feasible solution search (FSSPSO) is applied in order to optimize the reactive power dispatch, taking into consideration the reactive power requirement at point of common coupling (PCC), while active power losses are minimized in a WF. The reactive power requirement at PCC is included as a restriction problem and is dealt with feasible solution search. Finally an individual set point, particular for each wind turbine (WT), is found. The algorithm is tested in a WF with 12 WTs, taking into consideration different control options and different active power output levels.     

A model of substitution in a competitive market

A dynamic model of technological substitution is developed which is a generalization of the two-party substitution model of Fisher and Pry. Competition among an arbitrary number of technologies is modeled as a sequence of pair-wise interactions. The intent is to reproduce the findings of Marchetti, who has observed that a large variety of historical substitutions follow a logistic pattern.     

An effortless hybrid method to solve economic load dispatch problem in power systems

This paper proposes a new approach and coding scheme for solving economic dispatch problems (ED) in power systems through an effortless hybrid method (EHM). This novel coding scheme can effectively prevent futile searching and also prevents obtaining infeasible solutions through the application of stochastic search methods, consequently dramatically improves search efficiency and solution quality. The dominant constraint of an economic dispatch problem is power balance. The operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones (POZ), network loss are considered for practical operation. Firstly, in the EHM procedure, the output of generator is obtained with a lambda iteration method and without considering POZ and later in a genetic based algorithm this constraint is satisfied. To demonstrate its efficiency, feasibility and fastness, the EHM algorithm was applied to solve constrained ED problems of power systems with 6 and 15 units. The simulation results obtained from the EHM were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time.     

Biobjective power dispatch using goal-attainment method and adaptive polynomial networks

This paper presents an integrated approach that combines goal-attainment (GA) and adaptive polynomial networks (APN) to real-time biobjective power dispatch. The goals considered are fuel cost and the environmental impact of multiple emissions. The complicated relationships between the input (power demand and operator's economic and emission preferences) and the output (power generated by each generator) can be efficiently modeled by the APN. Moreover, the APN can rapidly provide an accurate estimate of the real-time dispatch results for the power demand and the operator's preferences. The effectiveness of the proposed approach has been demonstrated by the IEEE 30-bus six-generator and the practical Taipower 388-bus 27-generator systems. Test results reveal that the proposed approach achieves significant savings in computation time and reduces the complexity of the real-time power dispatch. Furthermore, the proposed APN outperforms the artificial neural networks (ANNs) method, in both developing the model and estimating the power generated by each generator.     

Capacity commitment and price volatility in a competitive electricity market

The long lead time required to add new capacity in the electricity generation industry means that daily demands are necessarily served by capacity already installed. However, in a competitive market, even if the installed capacity was designed to serve the projected demands, frequent surpluses and occasional full utilization inevitably lead to price volatility. This paper develops a two-stage model of the generation market in which capacity construction occurs in stage 1, before demand realization, and price determination occurs in stage 2, when the equilibrium price ensures that the realized demand does not exceed the installed capacity. We show that price volatility and price spikes are inevitable, and that while price capping can mitigate high and volatile prices, it causes unmet demands and reduction in system reliability. This paper accentuates the interdependence among generating capacity, price volatility and service reliability, a primary cause of concern in the debate on electricity market reform.     

Optimal dispatch of nearly-zero carbon integrated energy system considering waste incineration plant-carbon capture system and market mechanisms

Considering the waste incineration plant-carbon capture (WIP-CC) system and market mechanisms, the optimal dispatching strategy of nearly-zero carbon integrated energy system (NZC-IES) is proposed. The incineration of large amounts of Municipal Solid Waste can result in significant carbon emission and air pollution. However, lack of consideration of mechanisms for carbon processing and environmental protection will prevent the development of NZC-IES. Accordingly, a novel mixed integer nonlinear mathematical model, NZC-IES, is established that minimizes the total cost and controls carbon emission and air pollution. Unlike previous studies of optimal dispatching for WIP-CC system, the climate and health costs of WIP and the social costs of energy sources for CC are considered. A model of adjustable thermoelectric ratios for WIP and a model for CC with the storage tanks are established to enhance the flexibility of the thermoelectric output. Also, demand response model and ladder-type carbon trading model are developed to serve for NZC-IES. Case studies reveal that the proposed optimal dispatching strategy can realize the waste to energy utilization and low-carbon emission with economic performance.     

Optimal retailer bidding in a DA market - a new method considering risk and demand elasticity

This paper presents a new method to determine the optimal demand function for a retailer in power markets. It assumes that the retailer purchases the energy from either the day-ahead or the regulation market and sells it to the end users through fixed and/or real-time pricing contracts. The load is assumed to be price sensitive and the retailer to be price-taker. Through participation in the market and managing its risk, the retailer attempts to maximize its profit. The proposed method is tested on a typical power market.