Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs

This paper proposes a nodal ant colony optimization (NACO) technique to solve profit based unit commitment problem (PBUCP). Generation companies (GENCOs) in a competitive restructured power market, schedule their generators with an objective to maximize their own profit without any regard for system social benefit. Power and reserve prices become important factors in decision process. Ant colony optimization that mimics the behavior of ants foraging activities is suitably implemented to search the UCP search space. Here a search space consisting of optimal combination of binary nodes for unit ON/OFF status is represented for the movement of the ants to maintain good exploration and exploitation search capabilities. The proposed model help GENCOs to make decisions on the quantity of power and reserve that must be put up for sale in the markets and also to schedule generators in order to receive the maximum profit. The effectiveness of the proposed technique for PBUCP is validated on 10 and 36 generating unit systems available in the literature. NACO yields an increase of profit, greater than 1.5%, in comparison with the basic ACO, Muller method and hybrid LR-GA.     

An ICA based approach for solving profit based unit commitment problem market

With the advent of paralleling and implementation of restructuring in the power market, some routine rules and patterns of traditional market should be accomplished in a way different from the past. To this end, the unit commitment (UC) scheduling that has once been aimed at minimizing operating costs in an integrated power market, is metamorphosed to profit based unit commitment (PBUC) by adopting a new schema, in which generation companies (GENCOs) have a common tendency to maximize their own profit. In this paper, a novel optimization technique called imperialist competitive algorithm (ICA) as well as an improved version of this evolutionary algorithm are employed for solving the PBUC problem. Moreover, traditional binary approach of coding of initial solutions is replaced with an improved integer based coding method in order to reduce computational complexity and subsequently ameliorate convergence procedure of the proposed method. Then, a sub-ICA algorithm is proposed to obtain optimal generation power of thermal units. Simulation results validate effectiveness and applicability of the proposed method on two scenarios: (a) a set of unimodal and multimodal standard benchmark functions, (b) two GENCOs consist of 10 and 100 generating units.     

GENETIC ALGORITHM MODEL FOR PROFIT MAXIMIZATION OF GENERATING COMPANIES IN DEREGULATED ELECTRICITY MARKETS

In deregulated and rapidly changing electricity markets, there is strong interest on how to solve the new price-based unit commitment (PBUC) problem used by each generating company to optimize its generation schedule in order to maximize its profit. This article proposes a genetic algorithm (GA) solution to the PBUC problem. The advantages of the proposed GA are: 1) flexibility in modeling problem constraints because the PBUC problem is not decomposed either by time or by unit; 2) smooth and easier convergence to the optimum solution thanks to the proposed variable fitness function which not only penalizes solutions that violate the constraints but also this penalization is smoothly increasing as the number of generations increases; 3) easy implementation to work on parallel computers, and 4) production of multiple unit commitment schedules, some of which may be well suited to situations that may arise quickly due to unexpected contingencies. The method has been applied to systems of up to 120 units and the results show that the proposed GA constantly outperforms the Lagrangian relaxation PBUC method for systems with more than 60 units. Moreover, the difference between the worst and the best GA solution is very small, ranging from 0.10% to 0.49%.     

Mixed Biogeography-Based Optimization for GENCOs’ Maintenance Scheduling in Restructured Power Systems

Power industry restructuring has brought new challenges to the generation unit maintenance scheduling problem. Maintenance scheduling establishes the outage time scheduling of units in a particular time horizon. In the restructured power systems, the decision-making process is decentralized where each generating company (GENCO) tries to maximize its own benefit. Therefore, the principle to draw up the unit maintenance scheduling is different from the traditional centralized power systems. The objective function for GENCOs is to minimize his maintenance investment loss. Therefore, he hopes to put its maintenance on the weeks when the market-clearing price is lowest so that maintenance investment loss descends. This paper addresses the unit maintenance scheduling problem of GENCOs in restructured power systems. The problem is formulated as a mixed integer programming problem, and it is solved by using an optimization method known as biogeography-based optimization (BBO). BBO is simple to implement in practice and requires a reasonably small amount of computing time and a small amount of data communication. BBO has been tested by applying it to a GENCO with three generating units. This model consists of an objective function and related constraints, e.g., maintenance window, generation capacity, load and network flow. The simulation result of this method is compared with a classic method. The outcome is very encouraging and proves that BBO is powerful for minimizing GENCOs’ objective function.     

Multi-agent modeling for solving profit based unit commitment problem

Profit based unit commitment problem (PBUC) from power system domain is a high-dimensional, mixed variables and complex problem due to its combinatorial nature. Many optimization techniques for solving PBUC exist in the literature. However, they are either parameter sensitive or computationally expensive. The quality of PBUC solution is important for a power generating company (GENCO) because this solution would be the basis for a good bidding strategy in the competitive deregulated power market. In this paper, the thermal generators of a GENCO is modeled as a system of intelligent agents in order to generate the best profit solution. A modeling for multi-agents is done by decomposing PBUC problem so that the profit maximization can be distributed among the agents. Six communication and negotiation stages are developed for agents that can explore the possibilities of profit maximization while respecting PBUC problem constraints. The proposed multi-agent modeling is tested for different systems having 10–100 thermal generators considering a day ahead scheduling. The results demonstrate the superiority of proposed multi-agent modeling for PBUC over the benchmark optimization techniques for generating the best profit solutions in substantially smaller computation time.     

NODAL-BASED ANT COLONY OPTIMIZATION FOR PROFIT MAXIMIZATION OF GENCOS IN A DISTRIBUTED CLUSTER MODEL

In the deregulated electricity market, each generating company has to maximize its own profit by committing to a suitable generation schedule termed profit-based unit commitment (PBUC). This article proposes a nodal ant colony optimization (NACO) solution to the PBUC problem. This method has better convergence characteristics in obtaining an optimum solution. The proposed approach uses a cluster of computers performing parallel operations in a distributed environment for obtaining the PBUC solution. The time complexity and the solution quality, with respect to the number of processors in the cluster, are thoroughly tested. The method has been applied to systems of up to 120 units, and the results show that the proposed NACO in a distributed cluster consistently outperforms the other methods that are available in the literature.     

Performance comparison of enhanced PSO and DE variants for dynamic energy/reserve scheduling in multi-zone electricity market

During the last decade, energy regulatory policies all over the globe have been influenced by the introduction of competition. In a multi-area deregulated power market, competitive bidding and allocation of energy and reserve is crucial for maintaining performance and reliability. The increased penetration of intermittent renewable generation requires for sufficient allocation of reserve services to maintain security and reliability. As a result the market operators and generating companies are opting for market models for joint energy and reserve dispatch with a cost minimization/profit maximization goal. The joint dispatch (JD) problem is more complex than the traditional economic dispatch (ED) due to the additional constraints like the reserve limits, transmission limits, area power balance, energy-reserve coupling constraints and separate sectional price offer curves for both, energy and reserve.The present work proposes a model for the joint static/dynamic dispatch of energy and reserve in deregulated market for multi-area operation using enhanced versions of particle swarm optimization (PSO) and differential evolution (DE). A parameter automation strategy is employed in the classical PSO and DE algorithms (i) to enhance their search capability; (ii) to avoid premature convergence; and (iii) to maintain a balance between global and local search. The performance of enhanced PSO and DE variants is compared for single/multi-area power systems for static/dynamic operation, taking both linear and non-smooth cost functions. The proposed approach is validated on two test systems for different demands, reserve requirements, tie-line capacities and generator outages.     

Mixed integer programming of multiobjective hydro-thermal self scheduling

This paper presents a method for hydro-thermal self scheduling (HTSS) problem in a day-ahead joint energy and reserve market. The HTSS is modeled in the form of multiobjective framework to simultaneously maximize GENCOs profit and minimize emissions of thermal units. In the proposed model the valve loading effects which is a nonlinear problem by itself is linearized. Also a dynamic ramp rate of thermal units is used instead of a fix rate leading to more realistic formulation of HTSS. Furthermore, the multi performance curves of hydro units is developed and prohibited operating zones (POZs) of thermal unit are considered in HTSS problem. Also, in the proposed framework, the mixed integer nonlinear programming (MINLP) of HTSS is converted to mixed integer programming (MIP) problem that can be effectively solved by optimization softwares even for real size power systems. The lexicographic optimization and hybrid augmented-weighted ?-constraint technique is implemented to generate Pareto optimal solutions. The best compromised solution is adopted either by using a fuzzy approach or by considering arbitrage opportunities to achieve more profit. Finally, the effectiveness of the proposed method is studied based on the IEEE 118-bus system.     

Optimal generation scheduling based on AHP/ANP

This paper proposes an application of the analytic hierarchy process (AHP) and analytic network process (ANP) for enhancing the selection of generating power units for appropriate price allocation in a competitive power environment. The scheme addresses adequate ranking, prioritizing, and scheduling of units before optimizing the pricing of generation units to meet a given demand. In the deregulated environment, the classical optimization techniques will be insufficient for the above-mentioned purpose. Hence, by incorporating the interaction of factors such as load demand, generating cost curve, bid/sale price, unit up/down cost, and the relative importance of different generation units, the scheme can be implemented to address the technical and nontechnical constraints in unit commitment problems. This information is easily augmented with the optimization scheme for an effective optimal decision. The scheme proposed is tested using the IEEE 39-bus test system.     

基于预期完成率的月内滚动机组组合研究

为保障月度交易计划执行,提出了一种基于预期完成率的月内滚动机组组合方法,实现机组开停方式、电量计划完成情况和后续供需形势之间的有效匹配。为避免发电机组开停方式频繁调整,定义了发电厂预期完成率评价指标,以量化原发电机组组合方式下电量计划执行风险。若执行风险超过预期,则启动月内滚动机组组合。月内滚动机组组合以保障月度交易计划执行且各发电厂完成率均衡为优化目标,综合考虑电力电量平衡、网络传输能力、机组出力范围等约束约束,能够对当月后续运行日的发电机组组合方式优化调整。最后,基于IEEE-30节点系统构造的算例表明,预期完成率指标能够避免发电机组组合方式的频繁调整,保障发电厂月度交易计划可靠执行。     

Stochastic unit commitment problem

The electric power industry is undergoing restructuring and deregulation. We need to incorporate the uncertainty of electric power demand or power generators into the unit commitment problem. The unit commitment problem is to determine the schedule of power generating units and the generating level of each unit. The objective is to minimize the operational cost which is given by the sum of the fuel cost and the start‐up cost. In this paper we propose a new algorithm for the stochastic unit commitment problem which is based on column generation approach. The algorithm continues adding schedules from the dual solution of the restricted linear master program until the algorithm cannot generate new schedules. The schedule generation problem is solved by the calculation of dynamic programming on the scenario tree.     

A method for classifying offer strategies observed in an electricity market

The idea that large-scale generating units will operate at marginal cost when given the ability to offer their power for sale in a uniform price auction is at best wishful thinking. In fact, both real and experimental data show that the more uncertainty a supplier faces (e.g., load uncertainty, uncertainty of other suppliers, etc.), the more they will hedge their profits through higher than marginal cost offers and through withholding units if permitted. This makes predicting unit commitment and dispatch ahead of time difficult. This paper explores characteristics of software agents that were designed based on the outcome of human subject experiments on a uniform price auction with stochastic load. The agent behavior is compared to the behavior of the subjects. Both subject and agent behavior is classified based on the data. Differences and similarities are noted and explained. Based on the result of the simulation, a model was suggested to explain an offer submitted in deregulated markets based on double layer diffusion.     

Profit based unit commitment: A parallel ABC approach using a workstation cluster

This paper proposes a parallel artificial bee colony (PABC) approach for committing generating units thereby maximizing the profit of generation companies. Profit based unit commitment (PBUC) must be obtained in a short time even though there is an increase in generating units. Nowadays, computing resources are available in plenty, and effective utilization of these resources will be advantageous for reducing the time complexity for a large scale power system. Here, the message passing interface based technique is used in the PABC algorithm in distributed and shared memory models. The time complexity and the solution quality with respect to the number of processors in a cluster are thoroughly analyzed. PABC for PBUC is tested for a power system ranging from 10 to 1000 generating units. Also the PABC is validated for economic dispatch and the unit commitment problem in a traditional power system on 40 and 10 unit systems, respectively.     

Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations

In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units’ contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany's (GENCO's) profit maximization and thermal units’ emission minimization. Accordingly, the ɛ-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The performance of the presented method is verified in different case studies. The results obtained from ɛ-constraint method is compared with those reported by weighted sum method, evolutionary programming-based interactive Fuzzy satisfying method, differential evolution, quantum-behaved particle swarm optimization and hybrid multi-objective cultural algorithm, verifying the superiority of the proposed approach.     

Behavioral simulation and optimization of generation companies in electricity markets by fuzzy cognitive map

Simulation can be used in a wide range of applications in an electricity market. There are many reasons that market players and regulators are very interested in anticipating the behavior of the market. Behavior of a generation company (GENCO) in electricity market is an important factor that affects the market behavior. Several factors affect the behavior of a GENCO directly and indirectly. In this study, a new approach based on fuzzy cognitive map (FCM) is introduced to model and simulate GENCO’s behavior in the electricity market with respect to profit maximization. FCM helps the decision makers to understand the complex dynamics between a certain strategic goal and the related factors. This paper examines how effective factors affect on a GENCO’s profit. To identify key factors relevant to the goal, a FCM is built and then analyzed. To analyze this problem, two cases as simple FCM and weighted FCM are considered. Simple FCM shows how the determined factors affect on goal. A hidden pattern is obtained by this case. Weighted FCM helps sensitivity analysis of the model. In addition, the weighted FCM is used usefully to clearly measure the composite effects resulting from changes of multiple factors. This application is shown by two different case studies. This is the first study that models and simulates the behavior of GENCO in electricity market with respect to profit maximization.     

Profit based unit commitment using memetic binary differential evolution algorithm

Profit-based unit-commitment problem (PBUCP) is a notable combinatorial optimizing problem faced in the deregulated power industry. The PBUCP finds the best profitable solution by committing and scheduling the thermal generating units efficiently. To solve the PBUCP, a new memetic binary differential evolution algorithm is proposed which considers binary differential evolution (BDE) algorithm as global search operator to improve the exploration aspect and binary hill-climbing (BHC) algorithm as local search operator to improve the exploitation aspect. A binary differential evolution algorithm is introduced whereby a new mutation strategy is implemented. A novel BHC algorithm makes priority-based perturbations on unit’s status to improve the global best solution searched by the BDE algorithm alone. A new excessive unit de-commitment strategy based on priority and total profit is also proposed. The power to committed units is allocated based on priority of units. The efficacy of algorithms has been researched on the PBUCP test systems comprising of 10-, 40- and 100-units over a time horizon. The outcomes of the proposed algorithms are compared with previously known best solutions. Simulated outcomes achieved by the proposed algorithms compete with the already reported algorithms to solve the PBUCP. Wilcoxon signed-rank test proves the predominance of the proposed algorithms statistically.     

Design of input vector for day-ahead price forecasting of electricity markets

In new deregulated electricity market, price forecasts have become a fundamental input to an energy company’s decision making and strategy development process. However, the exclusive characteristics of electricity price such as non-stationarity, non-linearity and time-varying volatile structure present a number of challenges for this task. In spite of all performed research on this area in the recent years, there is still essential need for more accurate and robust price forecast methods. Besides, there is a lack of efficient feature selection technique for designing the input vector of electricity price forecast. In this paper, a new two-stage feature selection algorithm composed of modified relief and mutual information (MI) techniques is proposed for this purpose. Moreover, cascaded neural network (CNN) is presented as forecast engine for electricity price prediction. The CNN is composed of cascaded forecasters where each forecaster is a neural network (NN). The proposed feature selection algorithm selects the best set of candidate inputs which is used by the CNN. The proposed method is examined on PJM, Spanish and Ontario electricity markets and compared with some of the most recent price forecast techniques.     

Electricity price classification using extreme learning machines

Forecasting electricity prices has been a widely investigated research issue in the deregulated power market scenario. High price volatilities, price spikes caused by a number of factors such as weather uncertainty, fluctuating fuel prices, transmission bottlenecks, etc., make the task of accurate price forecasting a formidable challenge for the market participants. A number of models have been proposed by researchers; however, achieving high accuracy is always not possible. In some specific applications such as self-scheduling by demand side participants, certain price thresholds are more useful than accurate price forecasts. In this paper, we have investigated the application of a novel neural network-based technique called extreme learning machine for the problem of classification of future electricity prices with respect to certain price thresholds. Different models corresponding to different lead times are developed and tested with data corresponding to Ontario and PJM markets. It is observed that classification with ELM is fast, less sensitive to user defined parameters and easily implementable.     

Price strategies in dynamic duopolistic markets with deregulated electricity supplies using mixed strategies

While effective competition can force service providers to seek economically efficient methods to reduce costs, the deregulated electricity supply industry still allows some generators to exercise market power at particular locations, thereby preventing the deregulated power market to be perfectly competitive. In this paper, we investigate the interdependence of pricing mechanisms and strategy behaviors of the suppliers. A multiperiod dynamic profit-maximizing problem is converted to a bimatrix game that is solved in the framework of mixed strategies. By this procedure, we have at least one Nash solution. Instead of considering only perfectly competitive price and monopoly price, we introduce other prices between these two to simulate the real market better. Numerical examples show that the new entrant that maximizes its profit will not choose the perfectly competitive price even as an entry price.