Swarm intelligence-based strategic bidding in competitive electricity markets

In the competitive electricity markets, formation of supply bid is one of the main concerns, where suppliers have to maximise their profit under incomplete information of other competing generators. An environment is described in which suppliers bid strategically to sell electricity in a pool market. The bidding decision is optimised from a single supplier's viewpoint in both block-bid and linear-bid models of an electricity market. To include uncertain behaviour of other competing suppliers, two different probabilistic models are used. Their bids are constructed using probability distribution functions obtained from the decision-maker's observations of historical market data. Single supplier's decision-making problem is solved by a modern population-based heuristic algorithm, known as particle swarm optimisation (PSO). Search procedure of PSO is based on the concept of combined effect of cognitive and social learning of the members in a group. The effectiveness of the proposed method is tested with examples and the results are compared with the solutions obtained using the genetic algorithm approach.     

Towards reactive power markets

Two major contributions are discussed in this part of a two-part set. first, it presents a new concept of market reactive power requirement (qreq) for every participant in both bilateral and pool markets. this qreq is defined as the reactive power loss allocation to each bilateral transaction using current adjustment factor (CAF) method that has been proposed in part 1. when a transaction is allowed to provide reactive power more than its qreq, it contributes to system reactive power needs and it is considered a reactive power provider. on the other hand, if it cannot produce its qreq, it relies on other reactive power sources to ship its contracted power and it is considered a reactive power consumer even though it may generate some reactive power. the concept then is extended to participants in a pool market. secondly, this paper proposes a new general concept of differentiated reactive power requirements for every participant in electricity markets. it assumes that qreq of a participant depends on its allocated losses using any appropriate loss allocation method. the proposals help system operators and market participants to trade reactive power as well as real power effectively and fairly. the simulation results show validity and consistency of the proposals.     

Market power analysis in the oligopoly electricity markets under network constraints

Market power (MP) analysis under network constraints is a specific feature of electricity markets and, as well, one of the major concerns of the regulators. The goal, in the analysis, is to assess the impacts of network constraints on the market outcomes because of the strategic behaviour of the producers that may or may not take advantage of the knowledge of the network structure and of the constraints. In this work, different factors to the MP that can be used by the strategies of the producers are studied and the impacts of the network constraints in providing additional opportunities to exert MP are considered. The ex-ante analysis of the market is undertaken resorting to a supply function equilibrium (SFE) for which the authors propose a new approach for deriving the best slope for slope bidding strategy by an equivalent intercept iteration method. A set of indices, specifically devised for considering network impacts, are proposed to assess the MP of the producers in a constrained market. The application of the proposed approach is shown and tested on an IEEE 30 bus system.     

Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets

This article presents a metaheuristic approach, the binary whale optimization algorithm (BWOA), to solve complex, constrained, non-convex, binary-nature profit-based unit commitment (PBUC) optimization problems of a price-taking generation company (GenCo) in the electricity market. To simulate the binary-nature PBUC problem, the continuous, real-value whale position/location is mapped into binary search space through various transfer functions. This article introduces three variants of BWOA using tangential hyperbolic, inverse tangent (arctan) and sigmoidal transfer functions. The effectiveness of the BWOA approaches is examined in test systems with different market mechanisms, i.e. an energy-only market, and energy and reserve market participation with different reserve payment methods. The simulation results are presented, discussed and compared with other existing approaches. The convergence characteristics, solution quality and consistency of the results across different BWOA variants are discussed. The superiority and statistical significance of the proposed approaches with respect to existing approaches is also presented.     

Dynamic price forecast in a competitive electricity market

Price forecast is a key issue in competitive electricity markets. It provides useful information for the market players and the regulators, in both short and long run. Different approaches have been proposed and implemented. A new dynamic approach for forecasting the market price of electricity in the short term is proposed. The price dates are first clustered according to different types of daily profiles and then, given a proper function representing the trend in price, the set of unknown parameters are identified based on the zeroing of a Lyapunov function. The forecast can be dynamically updated with the latest data available. Higher weight can be attributed to this data in determining the future prices. The proposed approach is validated with reference to real systems in the form of the Italian, New England and New York electricity markets. In addition, an extensive price forecast is provided for the Italian market, an example of a young market that is rather difficult to predict patterns for.     

Static hedging of weather and price risks in electricity markets

Optimization and Engineering - We present the closed-form solution to the problem of hedging price and quantity risks for energy retailers (ER), using financial instruments based on electricity...     

Hydroelectric power in emissions-constrained electricity generation

The positioning of generating plant in rank (merit) order has been based traditionally on minimised system economic cost. If regulations were introduced to place a financial penalty on gaseous emissions from fossil-fuelled power stations, the result would be an increased economic burden on such generating plant. This paper examines the effect on merit order of the imposition of a tax on carbon dioxide emissions, and looks in particular at the potential role to be played by hydroelectric power     

Voltage stability constrained optimal dispatch in deregulated power systems

Voltage stability is a challenging problem for independent system operators because of less regulation in power flow patterns and more intensive use of available transmission facilities in deregulated power systems. An equitable and effective scheme for rescheduling pool generation and adjusting contract transactions is proposed in order to ensure adequate voltage stability margin (VSM). A linear optimisation methodology with the objective of minimising the social benefit loss because of generation rescheduling and transaction curtailment is formulated with consideration of the static functional operating constraints and VSM requirements with respect to normal condition and contingencies, and then solved by a linear programming technique. Since the pattern of load increase is difficult to be predicted in new market environment, the system load-ability of the locally closest bifurcation is computed and used as the VSM in the proposed method. The effectiveness of the proposed method has been verified using a modified 39-bus New England test system. A study on the effects of post-contingency corrective capability of the system on social benefit loss and voltage stability performance is also presented.     

Methods for customer and demand response policies selection in new electricity markets

Different methodologies are available for clustering and classification purposes. The objective of the research is to prove the capability of self-organising maps (SOMs) to classify customers and their response potential from distributor, commercialiser, or customer electrical demand databases, with the help of load response modelling methodologies as support tools. The search for customer suitability is restricted to day-ahead and real-time products, in which interest is growing in developed countries. Therefore customer demand and response (demand and distributed generation policies) have been tested and compared with price curves. Both steps have been performed through SOMs. The results clearly show the capability of this approach to improve data management and easily to find coherent policies to accomplish cleared-demand offers in different prices scenarios.     

Optimal distributed generation location using mixed integer non-linear programming in hybrid electricity markets

This study presents mixed integer non-linear programming (MINLP) approach for determining optimal location and number of distributed generators in hybrid electricity market. For optimal location of distributed generation (DG), first the most appropriate zone has been identified based on real power nodal price and real power loss sensitivity index as an economic and operational criterion. After identifying the suitable zone, mixed integer non-linear programming approach has been applied to locate optimal place and number of distributed generators in the obtained zone. The non-linear optimisation approach consists of minimisation of total fuel cost of conventional and DG sources as well as minimisation of line losses in the network. The pattern of nodal real and reactive power prices, line loss reduction and fuel cost saving has been obtained. The results have also been obtained for pool electricity market model for comparison. The impact of demand variation on the results has also been obtained for both the market models. The proposed MINLP-based optimisation approach has been applied for IEEE 24 bus reliability test system.     

无功的数值计算方法

本文从无功的物理意义出发，提出一种精确计算有功和无功的计算方法。提出了在无功计算中常存在的一些错误概念。     

An econometric analysis of product variety impact on competitive market conduct in consumer goods markets

Decisions on product variety are a central part of the (strategic) marketing planning process of many consumer goods manufacturers. However, we have only limited information about the effects of product variety on competitive market conduct and profitability. In this paper, we introduce a simple econometric methodology for studying market conduct in prices and variety between rival brands of consumer goods markets. Our study follows the recent trend in empirical industrial organization, it is fully structural and starts from the specification of demand and supply functions. We introduce a number of different game theoretic regimes and characterize the equilibrium of each of these games. The equilibrium of each game is considered to be unique. On the basis of non-nested model selection, we can identify the form of competitive market conduct that is most suitable for the underlying data. Our empirical study identifies Nash behavior in pricing and collusive behavior in variety among the two leading brands in the market. The estimated parameters offer theoretically founded insight into the competitive rules in the market and the impact of prices and variety on profits.     

Risk optimization: siting of nuclear power electricity generating units

Radiological doses from nuclear power plant accidents can extend to great distances away from the site. These technical and perceived risks of nuclear power generation are addressed in an optimization of the siting of additional nuclear units primarily in the middle Atlantic and northeastern United States of America. This study relies on a constrained optimization of long-term latent cancer and early fatality effects from probable severe accident releases to the public. The results provide policy perspectives that would benefit from nuclear power, but provide an inherent safety margin in the selection of sites for future generating units of electricity. The selection of sites from among the best is aided by these calculations for minimizing fatality risks at 17 existing sites. The mathematical model minimizes a linear function represented as the number of 1000 MWe units multiplied by the estimated severe accident site risk at existing sites assuming constraints on: the allowed percentage increase in total societal latent cancer risk as well as allowed percentage increase in early fatality risk; lower bounds for existing units at the sites; and an upper bound of doubling the units and risks. The early fatality risk was also varied for several levels of increase in latent cancer risk.     

Mapping matrices against vectorial frame in the instantaneous reactive power compensation

The original instantaneous reactive power or p-q theory has been used in a systematic way in the control of active power niters (APFs). When the APF is switched in parallel to a nonlinear and unbalanced load, the application of p-q theory allows a constant power compensation strategy. This means that after the APF connection, the supply instantaneous power is constant and presents the same value as the load average power. The use of other compensation strategies such as unity power factor, sinusoidal or balanced supply currents, among others, is also possible, as the development of another compensation strategy is possible within the p-q theory frame. A p-q theory reformulation without using mapping matrices is presented, which makes obtaining compensation currents easier. Finally, an exhaustive analysis of practical cases has been carried out at simulation and experimental levels through a laboratory prototype that has allowed verification of the proposed approach.     

An efficient imperialist competitive algorithm for scheduling in the two-stage assembly flow shop problem

This paper considers a two-stage assembly flow shop problem where m parallel machines are in the first stage and an assembly machine is in the second stage. The objective is to minimise a weighted sum of makespan and mean completion time for n available jobs. As this problem is proven to be NP-hard, therefore, we employed an imperialist competitive algorithm (ICA) as solution approach. In the past literature, Torabzadeh and Zandieh (2010 Torabzadeh, E., and M. Zandieh. 2010. “Cloud theory-based Simulated Annealing Approach for Scheduling in the Two-stage Assembly Flow Shop.” Advances in Engineering Software 41: 1238–1243.[Crossref], [Web of Science ®] , [Google Scholar]) showed that cloud theory-based simulated annealing algorithm (CSA) is an appropriate meta-heuristic to solve the problem. Thus, to justify the claim for ICA capability, we compare our proposed ICA with the reported CSA. A new parameters tuning tool, neural network, for ICA is also introduced. The computational results clarify that ICA performs better than CSA in quality of solutions.     

A competitive memetic algorithm for the distributed two-stage assembly flow-shop scheduling problem

This article addresses the distributed two-stage assembly flow-shop scheduling problem (DTSAFSP) with makespan minimisation criterion. A mixed integer linear programming model is presented, and a competitive memetic algorithm (CMA) is proposed. When designing the CMA, a simple encoding scheme is proposed to represent the factory assignment and the job processing sequence; and a ring-based neighbourhood structure is designed for competition and information sharing. Moreover, some knowledge-based local search operators are developed to enhance the exploitation ability. The influence of parameter setting on the CMA is investigated using the analysis of variance method. Extensive computational tests and comparisons are carried out, which demonstrate the effectiveness of the proposed CMA in solving the DTSAFSP.     

Multi-objective optimization of steam power plants for sustainable generation of electricity

A unified framework that combines process simulation and multi-objective optimization is presented to simultaneously maximize the annual profit, while minimizing environmental impact (i.e., greenhouse gas emissions) of steam power plants with fixed flowsheet structures. The proposed methodology includes the selection of suitable primary energy sources (i.e., fossil fuels, biomass, biofuels, and solar energy) for sustainable electricity generation. For solving the problem of optimal selection of energy sources, a linear model is developed and included within a highly nonlinear simulation model for the parameter optimization of steam power plants that is solved by using genetic algorithms. This approach is robust and avoids making discrete decisions. Life cycle assessment technique is used to quantify the greenhouse gas emissions resulting from different combinations of energy sources and operating conditions of the power plants. The thermodynamic properties for liquid water and steam are calculated rigorously using the IAPWS-IF 97 formulation. An example problem of an advanced regenerative-reheat steam power plant is presented to illustrate the proposed method, which provides the Pareto optimal solutions, the types and amounts of primary energy sources as well as the optimal values of the operating conditions of the plant that simultaneously maximize the profit while minimizing environmental impact.