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This paper presents a comparison between two options for operating a wind park in combination with a pumped-storage hydro-plant. First, we analyze the behavior of a wind farm that goes to the electricity market having previously forecasted the wind speed, while accepting the deviation penalties these forecasts may incur. Second, we study the possibility of the wind farm not going to the market individually, but as part of a hydro-wind power plant. Considerations about the optimal size of the wind farm and the hydro-pumped storage plant have previously been analyzed in the literature. However, most of these papers do not analyze dynamic considerations. The dimensioning of the system is not studied in our paper; its main feature will instead be the consideration of the dynamic optimal control problem. The use of Pontryagin's Maximum Principle allows us to obtain a very efficient optimization algorithm. The hydro-plant is modelled in great detail, using a variable-head model for the pumped hydro-plant. Our study provides a useful tool for electricity companies. The algorithm is able to solve the problem of the day-ahead market, a problem that is not solved in other studies. [ABSTRACT FROM AUTHOR]

The integration of solar photovoltaic (PV) into the electric vehicle (EV) charging system has been on the rise due to several factors, namely continuous reduction in the price of PV modules, rapid growth in EV and concerns over the effects of greenhouse gases. Despite the numerous review articles published on EV charging using the utility (grid) electrical supply, so far, none has given sufficient emphasis on the PV charger. With the growing interest in this subject, this review paper summarizes and update all the related aspects on PV–EV charging, which include the power converter topologies, charging mechanisms and control for both PV–grid and PV-standalone/hybrid systems. In addition, the future outlook and the challenges that face this technology are highlighted. It is envisaged that the information gathered in this paper will be a valuable one-stop source of information for researchers working in this topic. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for separation of bulk power system into several stable subsystems following a severe disturbance. An interconnected power system may become unstable due to wide area contingency when it is operated close to the stability boundaries as a result of increased demand, power industry restructuring and competition in the deregulated electricity markets. Meanwhile, large-scale power system controlled splitting is the last resort to prevent catastrophic cascading outages and wide area blackout. The proposed method of this paper reduces the huge initial search space of the islanding strategy to only interface boundary network by clustering the coherent generators and simplifying the network graph. Then, Backward Elimination Method (BEM) based on Primary Maximum Expansion Areas (PMEAs) has been proposed to generate all proper islanding scenarios in the simplified graph. The Newton–Raphson power flow method and Q–V modal analysis have been used to evaluate the steady state stability of the islands in each generated scenario. Binary Imperialistic Competitive Algorithm (BICA) has then been applied to minimize total load-generation mismatch considering no-isolated bus, voltage permitted range and static voltage stability constraints. Comprehensive discussions have been provided using the simulations on NPCC 68-bus test system. The results demonstrate the speed, effectiveness and capability of the proposed strategy to generate fast feasible splitting solutions considering static and dynamic stability. [ABSTRACT FROM AUTHOR]

Microgrid is a combination of distributed generators, storage systems, and controllable loads connected to low-voltage network that can operate either in grid-connected or in island mode. High penetration of power at distribution level creates such multiple microgrids. This paper proposes a two-level architecture for distributed-energy-resource management for multiple microgrids using multiagent systems. In order to match the buyers and sellers in the energy market, symmetrical assignment problem based on naíve auction algorithm is used. The developed mechanism allows the pool members such as generation agents, load agents, auction agents, grid agents, and storage agents to participate in market. Three different scenarios are identified based on the supply–demand mismatch among the participating microgrids. At the end of this paper, two case studies are presented with two and four interconnected microgrids participating in the market. Simulation results clearly indicate that the agent-based management is effective in resource management among multiple microgrids economically and profitably. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, a short-term load forecasting method is considered, which is based upon a flexible smooth transition autoregressive (STAR) model. The described model is a linear model with time varying coefficients, which are the outputs of a single hidden layer feedforward neural network. The hidden layer is responsible for partitioning the input space into multiple sub-spaces through multivariate thresholds and smooth transition between the sub-spaces. In this paper, we propose a new method to smartly initialize the weights of the hidden layer of the neural network before its training. A self-organizing map (SOM) network is applied to split the historical data dynamics into clusters, and the Ho–Kashyap algorithm is then used to obtain the separating planes'' equations. Applied to the electricity markets, the proposed method is better able to model the smooth transitions between the different regimes, which are present in the load demand series because of market effects and season effects. We use data from three electricity markets to compare the prediction accuracy of the proposed method with traditional benchmarks and other recent models, and find our results to be competitive. [Copyright &y& Elsevier]

This paper presents a new approach to design reactive power capacity markets. Under this approach annual auctions to procure reactive power capacity are conducted by the system operator. Two products are defined, reactive power generation and absorption capacity, which can be supplied by different independent VAR sources, such as generators, SVCs, capacitor banks and shunt reactors. Reactive power capacity is allocated using an optimization algorithm that matches capacity bids and system reactive power needs, for peak and low demand hours. The reactive power requirements are calculated taking into account system contingencies and the dynamic performance of the VAR sources under these situations. Another key proposal of the paper is the distribution of costs of the reactive power capacity market to the agents responsible for contingencies according to the use of the service by each market participant. This remuneration approach provides fair and efficient economic signals to all market participants. The applicability of the approach is illustrated with two case studies. [ABSTRACT FROM AUTHOR]

The use of Nash equilibria as strategic bidding solutions for players competing in a centralized electricity market has been explored in the literature. In addition, the existence of multiple market equilibria, both in pure and in mixed strategies, when system constraints are taken into account in the market solution, has been shown for particular test cases. This paper addresses the difficult problem, due to its exponential order, of finding multiple equilibria in centralized electricity markets. A systematic procedure which allows the analysis of multimachine systems is employed. Moreover, some conclusions concerning the existence of multiple market equilibria in real networks are made possible by the method presented here. The paper carefully explains the models and market assumptions under discussion and details the algorithmic procedure for the search method. The IEEE 30-bus and 57-bns systems are used as test cases. [ABSTRACT FROM AUTHOR]

In an electrical competitive market, load shedding decision support systems are needed to find the ways to process load shedding to satisfy both economic and technical conditions. This paper proposed the Fuzzy Analytic Hierarchy Process algorithm (Fuzzy-AHP) and fuzzy logic for load profile approach to determine the weight of the load nodes of the system and select the control strategy when the system operates at various load levels. Weights of the load nodes were calculated by triangular fuzzy numbers in the pair comparison. These results combined with the fuzzy logic for load profile to draw out the suitable control strategies for various load levels in case of emergency. The effectiveness of the algorithm was demonstrated through the experiment of load shedding of IEEE 37 bus compared with traditional AHP algorithm. [ABSTRACT FROM AUTHOR]

The efficient location identification of multiple line outages is critical to not only cascading failure elimination but also repair cost reduction. Most of existing methods, however, fail to handle location identification well. This failure typically occurs because the methods cannot overcome two challenges: the very limited number of phasor measurement units (PMUs) and the high computational complexity. This paper presents an efficient algorithm inspired by the ambiguity group theory to identify the locations of line outages with limited PMUs. Using 14-, 57-, 118-, 300-, and 2383-bus systems, our experimental study demonstrates that the proposed technique successfully identifies the most likely multiple line outages while attaining a 500 \times speedup when compared to the method of exhaustive search. [ABSTRACT FROM PUBLISHER]

This paper investigates the effect of a carbon price on wholesale electricity prices and carbon-pass-through rates in the states comprising the Australian National Electricity Market (NEM). The methodology utilize an agent-based model, which contains many features salient to the NEM including intra-state and inter-state transmission branches, regional location of generators and load centres and accommodation of unit commitment features. The model uses a Direct Current Optimal Power Flow (DC OPF) algorithm to determine optimal dispatch of generation plant, power flows on transmission branches and wholesale prices. The results include sensitivity analysis of carbon prices on wholesale prices and carbon pass-through rates for different states within the NEM. [ABSTRACT FROM AUTHOR]

Highlights: [•] In this paper a simple market clearing procedure is proposed. [•] It considers network constraints and the coupling among energy-reserve markets. [•] Two kinds of UC are performed hierarchically by using a hybrid algorithm. [•] A single framework emulates the process of market clearing in competitive environment. [•] Efficient parallelizable hierarchical solution, easy to program and to understand. [ABSTRACT FROM AUTHOR]

The cross-border trading among electricity markets in an interconnected multi-area system (e.g., in central Europe) and the integration of renewable resources (e.g., wind energy) have remarkably increased in recent years. To efficiently operate such system, a proper coordination among different areas is required. Within this context, a decentralized algorithm for market clearing is proposed in this paper to dispatch simultaneously energy and reserve under wind generation uncertainty and equipment failures. The proposed technique does not require a central operator but just a moderate interchange of information among neighboring areas. Additionally, the benefit of cross-border trading is studied. [ABSTRACT FROM PUBLISHER]

In a multimarket environment, a GENCO produces electricity subject to a number of factors, including physical and environmental constraints, together with trading strategies in the electricity market (EM), fuel market (FM) and carbon market (CM). To assist a GENCO to maximize its profits from EM, FM and CM, this paper proposes a dynamic decision making model with two consecutive stages. Fuzzy differential evolution algorithm is used to solve this decision-making problem. Taking transactions in the three interactive markets into account, the proposed model has been tested for a GENCO consisting of seven thermal units and a wind farm. A rational tradeoff between the profit-making and emission reduction has been demonstrated by the GENCO using the proposed model, indicating a well alignment with the intended goal of the introducing emission trading scheme (ETS). [ABSTRACT FROM PUBLISHER]

This paper presents two hybrid heuristic methods for reconfiguration of the radial electrical distribution system. The first stage of the proposed techniques is a circular minimum-branch-current updating mechanism to bypass the local optimum points. Then, by a circular neighbor-chain updating technique, the best known configuration is obtained. The proposed approaches have been tested on the six well-known electrical distribution networks. The results show that the proposed methods have remarkably achieved the best-known optimum solution with more reliability, accuracy and efficiency than many other published methods. [ABSTRACT FROM PUBLISHER]

Abstract: We present a novel transmission expansion planning model that explicitly considers a multi-year planning horizon. The proposed formulation is a mixed-integer linear problem that can be solved using commercially available software. The algorithm determines the best overall transmission expansion plan, reflecting both investment and operational costs. To analyze the results obtained from the model, we use a set of efficiency metrics to appraise the effect of the expansion among generators and demands, as well as congestion metrics to measure changes in nodal prices and line congestions. This paper also provides a case study that considers the expansion plan for a transmission system based on the one of mainland Spain. [Copyright &y& Elsevier]

Abstract: This paper presents a novel probabilistic algorithm for optimal reactive power provision in hybrid electricity markets. The proposed algorithm is a six-stage multiobjective nonlinear constrained optimization problem which takes into account load forecasting inaccuracies. Considering a set of probable forecasted loads, a three-component expected total market payment function is suggested being minimized as cost function of the first stage. Besides economic issues, expected voltage security margin, deviation from multilateral and pool based energy transactions, deviation from spinning reserve contracts, having adequate local reactive power reserve in each voltage control area of the system and transmission congestion probability are well thought out in stages 2–5 as technical aspects of the market. Finally, in the last stage, using different weighting factors to compromise between all objects, a probabilistic multiobjective function is presented to find the best reactive power market schedule. The proposed algorithm is applied on IEEE 24-bus test system. As a benchmark, Monte Carlo Simulation method is utilized to simulate the market of given period of time to evaluate results of the proposed algorithm, and satisfactory results are achieved. [Copyright &y& Elsevier]

Abstract: In open energy markets, the settlement process between distribution operators and traders is made on an hourly (or 15min) basis, while LV consumers’ billing data continues to result from monthly energy bills. In order to reconcile these two different realities, load profiling is used as a means to redistribute the consumed energy of each trader''s portfolio by hourly intervals, according to recorded consumption patterns. This paper presents a new clustering approach to derive typical load diagrams that can be used in the process. The algorithm uses real load diagrams obtained in measurement campaigns to define classes (in the billing information space) that maximize the compactness of the diagrams in each class. The methodology was developed in a project with EDP Distribution (the Portuguese distribution system operator) and the result was approved by the Regulatory Authority that adopted the proposed profiles for market use. [Copyright &y& Elsevier]

A generation firm in an electricity market may own multiple generators located at multiple locations. This paper generalizes the concept of transmission-constrained residual demand from a single generator's perspective to that of a generation firm. We calculate the derivative of a generation firm's inverse residual demand function, i.e., the Jacobian matrix, based on a multi-parameter sensitivity analysis of the optimal power flow solution, and characterize some of its properties. This Jacobian matrix provides valuable information, such as in characterizing a generation firm's profit maximizing strategy. We apply the bundle-Newton method utilizing the Jacobian matrix to find a generation firm's maximum profit. The effectiveness and performance of the algorithm is demonstrated with the IEEE 118-bus system example. The Jacobian matrix and the profit maximizing algorithm are helpful for market participants to bid into electricity markets, and for market monitors to analyze firm-based strategic behaviors. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel approach to optimize a generator's profit in an offer-based electricity market cleared by an optimal power flow (OPF) program. A generator's offer is optimized based on its transmission-constrained residual demand derivative (TCRDD), which avoids representing the full network model in the optimization. The TCRDD can be easily calculated based on a solved OPF without changing existing OPF algorithms and programs. As demonstrated with an IEEE 118-bus example, the TCRDD approach is computationally efficient. The TCRDD approach can help market participants to bid into electricity markets, and help market monitors to diagnose bidding behaviors in the presence of transmission constraints. [ABSTRACT FROM PUBLISHER]

Reserve services are an essential part of a standard market design and are under development in major electricity markets. This paper presents the development of differential evolution algorithm for optimal allocation of energy and spinning reserve. The optimization is performed in an integrated market with biddable spinning reserve and sectional price offer curves while considering all security and power systems constraints in steady state and system credible contingencies. The effectiveness of the proposed method is examined by application of the proposed algorithm on the IEEE 30-bus test system. Some comparisons are made with analytical and evolutionary methods in various cases. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology to solve the problem of distribution system planning, using a multi-objective optimization technique. In the proposed model the location and design of new elements and the upgrading of existing elements are considered. In the problem approach, investment and operation costs and network reliability are included in the objective function. This research topic is relevant to the electricity distribution companies, as it allows for new tools that bring the problem to real situations, such as considering multiple objectives, which has a positive impact on the finances and on operation of the system. In the model solution, an elitist non-dominated sorting algorithm (NSGA-II) is used, and to verify its efficiency a case of specialized literature is used, which corresponds to a distribution network of an electrical system. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal control (OC) problem that arise when a hydraulic system with fixed-head hydroplants is considered. In the frame of a deregulated electricity market, the resulting Hamiltonian for such OC problems is linear in the control variable and results in an optimal singular/bang-bang control policy. To avoid difficulties associated with the computation of optimal singular/bang-bang controls, an efficient and simple optimization algorithm is proposed. The computational technique is illustrated on one example. [ABSTRACT FROM AUTHOR]

Abstract: Several types of market equilibria approaches, such as Cournot, Conjectural Variation (CVE), Supply Function (SFE) or Conjectured Supply Function (CSFE) have been used to model electricity markets for the medium and long term. Among them, CSFE has been proposed as a generalization of the classic Cournot. It computes the equilibrium considering the reaction of the competitors against changes in their strategy, combining several characteristics of both CVE and SFE. Unlike linear SFE approaches, strategies are linearized only at the equilibrium point, using their first-order Taylor approximation. But to solve CSFE, the slope or the intercept of the linear approximations must be given, which has been proved to be very restrictive. This paper proposes a new algorithm to compute CSFE. Unlike previous approaches, the main contribution is that the competitors’ strategies for each generator are initially unknown (both slope and intercept) and endogenously computed by this new iterative algorithm. To show the applicability of the proposed approach, it has been applied to several case examples where its qualitative behavior has been analyzed in detail. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel technique for representing system security in the operation of decentralized electricity markets, with special emphasis on voltage stability. The market-clearing algorithm is modeled as voltage stability constrained-optimal power flow (VSC-OPF) problem for providing market solutions by means of a function of complying with the required voltage stability criteria. Benders’ decomposition is applied for solving the VSC-OPF incorporating post-contingency control actions, which is motivated by the improvement of computational efficiency using parallel processing. The proposed VSC-OPF framework also takes into consideration the bilateral contract information, which is integrated into the market-clearing process and, at the same time, the optimal pricing expressions through computing local marginal prices (LMPs) with respect to ensuring voltage stability are derived. VSC-OPF is tested on the IEEE 14-bus benchmark system and the results obtained, when compared to those obtained by means of a conventional OPF, show that the proposed technique is able to improve system security while yielding better market solutions and total transaction levels. [Copyright &y& Elsevier]

Abstract: This paper proposes an efficient method to solve the profit-based optimal self-scheduling of a hydroelectric company in the pool-based day-ahead electricity market by integrating cultural algorithm (CA) with feasible sequential quadratic programming (SQP). The proposed method is developed in such a way that CA is applied as a based level search, which can give a good direction to the optimal global region, and a local search SQP is used as a fine-tuning to determine the optimal solution at the final. Finally, optimal self-scheduling of hydro company with several cascaded hydro plants along a river basin is used to illustrate the effectiveness of the proposed method. Results from the proposed method are compared with those obtained by other optimization methods in terms of solution precision. It is shown that the proposed method is capable of yielding higher quality solutions. [ABSTRACT FROM AUTHOR]

Previous researches about electrical load time series data forecasting showed that the result was not satisfying. This paper elaborates the enhanced neuro-fuzzy architecture for the same application. The system uses Gaussian membership function (GMF) for Takagi-Sugeno fuzzy logic system. The training algorithm is Levenberg-Marquardt algorithm to adjust the parameters in order to get better forecasting system than the previous researches. The electrical load was taken from East Java-Bali from September 2005 to August 2007. The architecture uses 4 inputs, 3 outputs with 5 GMFs. The system uses the following parameters: momentum=0.005, gamma=0.0005 and wildness factor=1.001. The MSE for short term forecasting for January to March 2007 is 0.0010, but the long term forecasting for June to August 2007 has MSE 0.0011. [ABSTRACT FROM AUTHOR]

Abstract: Reactive power management is essential to transfer real energy and support power system security. Developing an accurate and feasible method for reactive power pricing is important in the electricity market. In conventional optimal power flow models the production cost of reactive power was ignored. In this paper, the production cost of reactive power and investment cost of capacitor banks were included into the objective function of the OPF problem. Then, using ant colony search algorithm, the optimal problem was solved. Marginal price theory was used for calculation of the cost of active and reactive power at each bus in competitive electric markets. Application of the proposed method on IEEE 14-bus system confirms its validity and effectiveness. Results from several case studies show clearly the effects of various factors on reactive power price. [Copyright &y& Elsevier]

Abstract: This paper develops optimal bidding strategies for a pumped-storage plant in a pool-based electricity market. In the competitive regime, when compared to simple hydro electric generator, profit of the pumped-storage plant is maximized by operating it as a generator when market clearing price is high and as a pump when the price is low. Based on forecasted hourly market clearing price, a multistage looping algorithm to maximize the profit of a pumped-storage plant is developed, considering both the spinning and non-spinning reserve bids and meeting the technical operating constraints of the plant. The proposed model is adaptive for the nonlinear three-dimensional relationship between the power produced, the energy stored, and the head of the associated reservoir. Different operating cycles for a realistic pumped-storage plant are considered and simulation results are reported and compared. [Copyright &y& Elsevier]

This paper presents a new algorithm of accurate buswise transmission loss allocation based on path-integrals. With rigorous theoretical analysis, a new path-integral method is developed by integrating the partial differential of the system loss along a path reflecting the transaction strategy. This path-integral enables us to remarkably enhance the accuracy in loss allocation with full consideration of nonlinearity. The accuracy has been further improved by using the ac power flow. Determining the integral path is discussed to reflect various situations of the power market. Given an integral path, the proposed algorithm provides a unique and accurate solution to the loss allocation. An effective method is proposed to reduce the computation time remarkably in the integration of bus-wise differential loss. The proposed algorithm has been tested and the results showed that online application is possible to large power systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for solving the unit commitment (UC) problem based on quantum-inspired evolutionary algorithm (QEA). The proposed method applies QEA to handle the unit-scheduling problem and the Lambda-iteration technique to solve the economic dispatch problem. The QEA method is based on the concept and principles of quantum computing, such as quantum bits, quantum gates and superposition of states. QEA employs quantum bit representation, which has better population diversity compared with other representations used in evolutionary algorithms, and uses quantum gate to drive the population towards the best solution. The mechanism of QEA can inherently treat the balance between exploration and exploitation and also achieve better quality of solutions, even with a small population. The proposed method is applied to systems with the number of generating units in the range of 10 to 100 in a 24-hour scheduling horizon and is compared to conventional methods in the literature. Moreover, the proposed method is extended to solve a large-scale UC problem in which 100 units are scheduled over a seven-day horizon with unit ramp-rate limits considered. The application studies have demonstrated the superior performance and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract  In a new competitive electricity market, accurate information should be shared to provide nondiscriminatory access to all participants. Key information to determine how much power can be shipped through the network is dubbed available transfer capability (ATC). This paper presents a methodology for the calculation of ATC, which is performed through a fuzzy logic approach to parallelizing contingency constrained optimal power flow (CCOPF). This algorithm may be used by utilities to optimize economy interchange for severe contingencies analyzed without disclosing details of their operating cost to competitors. In fact, the main objective of fuzzy multi-objective CCOPF is to determine the minimization of both the base-case (pre-contingency) operating cost and the post-contingency correction times as conflicting but fuzzy goals. Also, Benders decomposition is adopted to partition the fuzzy formulation with contingency constraints, which allows for post-contingency corrective rescheduling, motivated by the improvement of the computational efficiency using parallel processing. The IEEE-30 bus system is employed to test the proposed algorithm and the results are comprehensively demonstrated by a distinct comparison between the conventional optimal power flow and the CCOPF with respect to the same array of transactions, base-case, and generation/line outages. [ABSTRACT FROM AUTHOR]

In competitive electric energy markets, the power generation dispatch optimization is one of the most important missions among generation companies—how to respond to the markets, dispatch their units, and maximize profits. This paper proposes an approach to incorporate power contracts, which include call and put options, forward contracts, and reliability must-run contracts, into multi-area unit commitment and economic dispatch solutions. The proposed solution algorithm is based on adaptive Lagrangian relaxation, unit decommitment, and lambda-iteration methods. The problem formulation consists of three stages: 1) the incorporation of the power contracts, 2) the multi-area unit commitment, and 3) the multi-area economic dispatch. The proposed algorithm has been successfully implemented, and its testing results on modified IEEE test cases are promising. [ABSTRACT FROM AUTHOR]

In this paper, the primal-dual nonlinear interior point method has been used as a basis for the derivation of a novel supply function equilibrium (SFE) algorithm for modeling the strategic interactions in the electricity market, using the ac network model, which incorporates modeling of the transformer tap-ratio control. The algorithm is used to investigate the impact of transformer tap-ratio control on the electricity market equilibrium and the effect of the interactions between network constraints and transformer tap-ratio control. The interior point social welfare optimization problem is combined with the optimization problem for maximizing the profit of each strategic generating firm in the market. The final combined single-level SFE problem is solved iteratively based on solution techniques of the interior point method. Numerical examples illustrate the effect of network constraints and especially the impact of transformer tap-ratio control on market equilibrium. [ABSTRACT FROM AUTHOR]

Abstract: The optimal utilization of multiple combined heat and power (CHP) systems is a complicated problem that needs powerful methods to solve. This paper presents a harmony search (HS) algorithm to solve the combined heat and power economic dispatch (CHPED) problem. The HS algorithm is a recently developed meta-heuristic algorithm, and has been very successful in a wide variety of optimization problems. The method is illustrated using a test case taken from the literature as well as a new one proposed by authors. Numerical results reveal that the proposed algorithm can find better solutions when compared to conventional methods and is an efficient search algorithm for CHPED problem. [Copyright &y& Elsevier]

This paper describes the use of an interactive evolutionary algorithm for the identification and selection of direct load control actions in electrical distribution networks. The evolutionary algorithm accommodates a progressive articulation of the decision maker's preferences by changing aspiration or reservation levels used in the fitness assessment of the individuals in the population (load control strategies). Genetic operators have revealed as an adequate way to supply the evolutionary process with relevant information about the search results. Besides contributing to reduce the scope of the search, and thus the computer effort, this also enables the identification of solutions more in accordance with the decision maker's evolving preferences. [ABSTRACT FROM AUTHOR]

This paper presents an interior point algorithm based on a.c. network model for determining the Nash supply function equilibrium (SFE) of bid-based electricity markets. The SFE problem is considered as a bi-level game. At the first level, the problem begins with the formulation of an optimal power flow (OPF) interior point-based algorithm to handle the independent system operator (ISO) problem for maximising social welfare. This algorithm is based on the OPF with a.c. network transmission model taking into account all the operating aspects such as the generation capacity limits, bus voltage limits, transmission line constraints, network losses and especially the effect of the reactive power. The resulting Karush-Kuhn-Tucker (KKT) conditions of the problem at the first level are then reformulated using nonlinear complementarity constraints and incorporated as equality constraints in the second-level formulation for maximising the individual profit for each strategic generating firm. By employing a special nonlinear complementarity function, the complementarity constraints are then transformed into nonlinear algebraic expressions, thus the KKT conditions of the resulting combined problem can be derived. The final problem is then solved iteratively based on the solution techniques of the interior point algorithm. Numerical examples of a three-bus system, the IEEE 14-bus system and the IEEE 30-bus system, show that the algorithm can successfully determine the electricity market equilibrium with the a.c. network model. [ABSTRACT FROM AUTHOR]

Abstract: Optimal power flow (OPF) has been widely used in power system operation and planning. In deregulated environment of power sector, it is of increasing importance, for determination of electricity prices and also for congestion management. The classical methods are usually confirmed to specific cases of the OPF and do not offer great freedom in objective functions or the types of constraints that may be used. With a non-monotonic solution surface, classical methods are highly sensitive to starting points and frequently converge to local optimal solution or diverge altogether. This paper describes an efficient evolutionary programming based optimal power flow and compares its results with well known classical methods, in order to prove its validity for present deregulated power system analysis. [Copyright &y& Elsevier]

The assessment of the "power quality" and "voltage quality" in a deregulated electricity market has become a more and more critical point. As far as the voltage quality is concerned, the first step toward its assessment is the availability of measurement systems able to detect and classify the possible disturbances affecting the voltage itself. This task is quite difficult when transient disturbances are involved, and very complex algorithms have been proposed, mainly to classify these disturbances. This paper proposes a very simple detection algorithm, whose aim is to act as a real-time "trigger" for subsequent, more complex classification algorithms. A VI has been realized implementing this detection algorithm in real time, and the experimental results collected during several months of operation are reported. [ABSTRACT FROM AUTHOR]

Abstract: Electricity market restructuring, advances in energy generation technology and agreements on the reduction of global greenhouse gas emissions have paved the way for an increase in the use of distributed generation. This paper formulates and discusses a methodology for the optimal siting of distributed generators and reclosers a security and reliability constrained distribution network can accept. Optimal siting is determined by sensitivity analysis of the power flow equations. The sizing method for a set of loading conditions, generation penetration level and power factor is formulated as a security constrained optimization problem. The information on optimal generation sites is used further to optimize system reliability assessed via reliability indices calculation. A genetic algorithm is designed to solve for optimal recloser positions when distributed generators are deployed in a securely optimal manner. [Copyright &y& Elsevier]

The planning of electrical power distribution systems strongly influences the supply of electrical power to consumers. The problem is to minimize both the investment cost for feeder and substations, and the power-loss cost. When the substations can already provide enough power flow, then the problem reduces to minimize the total cost related to the feeders and their power-loss. The difficulty of dealing with this problem increases rapidly with its size (i.e., the number of customers). It seems appropriate to use heuristic methods to obtain suboptimal solutions, since exact methods are too much time consuming. In this paper, a simulated annealing algorithm is used. A set of numerical results are provided. [ABSTRACT FROM AUTHOR]

This paper presents an optimization algorithm, which provides generation economic dispatch minimizing simultaneously the cost of primary reserve services, while ensuring the secure operation of the power system in the presence of disturbances. The algorithm takes into consideration security constraints, which are extracted from the chain rules provided by decision trees. The proposed method is easy to implement in day-ahead and real-time balancing markets and is characterized from accuracy, versatility, and flexibility. Examples demonstrating application of the method on an actual power system model are presented and discussed. [ABSTRACT FROM AUTHOR]

Although spot price theory has been successfully practiced in some power markets worldwide, it has always been criticized for its lack of transparency. A competitive open power market requires transparent price signals, and each market participant has the right to query what has been charged and why. This paper presents a practical algorithm to give the detailed analysis of spot prices. By avoiding complicated sensitivity analysis of nonlinear programming, the implementation of the proposed algorithm is fast and highly efficient. Although the spot price can only be approximately decomposed by this algorithm, the accuracy of the result is still high due to its fully making use of the power system characteristics. Most important of all, the individual market participant can apply the proposed algorithm to evaluate the rationality of the price that he has to pay, while the complete sensitivity analysis of nonlinear programming cannot, since not all of the information necessary for the sensitivity analysis is available among the market participants. A well-known three-bus system and the IEEE 30-bus system are studied to illustrate the proposed algorithm. [ABSTRACT FROM AUTHOR]