An artificial neural network approach for revealing market competitors' behavior

For an electricity market player, obtaining a holistic viewpoint from the behavior of competitors is essential to determine its optimal bidding strategy. This paper proposes a novel approach for modeling and revealing the competitor's behavior from perspective of an intended player (IP). To this end, from perspective of IP, we define an Equivalent Rival (ER) whose behavior in the electricity market reflects the aggregation of behaviors of all individual competitors. It is assumed that IP and its ER participate in an equivalent market which its outcomes are approximately equal to those of the real market. The revealing procedure is designed as a two-stage Artificial Neural Network-based approach to estimate and predict the bids of ER after each run of the real market. Predicted bids of ER are used for the bidding strategy of IP. The proposed approach has been examined on two different case studies. In the first case study the aggregate supply curve of a market with 12 players has been obtained using the proposed approach and the result has been compared with a Bayesian inference approach. In the second case study a six-player electricity market is considered. The competitors' behavior has been revealed from perspective of an intended player using proposed approach and an optimal bidding strategy based on the proposed approach has been constructed. The results have been compared with those of a fuzzy Q-learning based optimal bidding strategy. The superiority of the proposed method in both case studies has been shown.fi=vertaisarvioitu|en=peerReviewed

Nash Equilibrium of Joint Day-ahead Electricity Markets and Forward Contracts in Congested Power Systems

Uncertainty in the output power of large-scale wind power plants (WPPs) can face the electricity market players with undesirable profit variations. Market players can hedge themselves against these risks by participating in forward contracts markets alongside the day-ahead markets. The participation of market players in these two markets affects their profits and also the prices and power quantities of each market. Moreover, limitations in the transmission grid can affect the optimal behavior of market players. In this paper, a Cournot Nash equilibrium model is proposed to study the behavior of market players in the forward contract market and the day-ahead electricity market in a congested power system with large-scale integration of WPPs. The proposed method is applied to a test system, and the results are discussed

How Does Large-scale Wind Power Generation Affect Energy and Reserve Prices?

Intermittent nature of wind power faced ISO and power producers with new challenges. Wind power uncertainty has increased the required reserve capacity and deployment reserve. Consequently, large-scale wind power generation increases ISO costs and consequently reserve prices. On the other hand, since wind power producers are price taker, large-scale wind power generation decreases residual demand and consequently decreases energy and reserve prices. In this paper, impacts of large-scale wind power generation on energy and reserve markets are studied. To this end, we need to know bids of power producers. But, bids of power producers are unknown and changes if wind power penetration is varied. To overcome this problem, first equilibrium of day-ahead energy market is computed at the presence of large-scale wind power generation considering hour-ahead deployment reserve market scenarios. Then, equilibrium of hour-ahead reserve market is computed considering results of day-ahead market. Finally, impacts of large-scale wind power generation on energy and reserve markets are studied at the markets equilibria. The presented model is applied to an 18-unit power system and the results are analyzed

Social welfare maximization with fuzzy based genetic algorithm by TCSC and SSSC in double-sided auction market

This paper presents a fuzzy-based genetic algorithm to maximize total system social welfare by best the placement and sizing of TCSC and SSSC devices, considering their investment cost in a double-sided auction market. To introduce more accurate modeling, the valve loading effects are incorporated into the conventional quadratic smooth generator cost curves. In addition, quadratic consumer benefit functions are integrated into the objective function to guarantee that locational marginal prices charged at the demand buses are less than, or equal to, the DisCos benefit, earned by selling the power to retail customers. The proposed approach utilizes fuzzy-based genetic algorithms for optimal scheduling of GenCos and DisCos, as well as optimal placement and sizing of SSSC and TCSC units. In addition, the Newton–Raphson approach is used to minimize the mismatch of the power flow equation. Simulation results on the modified IEEE 14-bus and IEEE 30-bus test systems (with/without line flow constraints, before and after the compensation) are used to examine the impact of SSSC and TCSC on total system social welfare improvement versus their cost. To validate the accuracy of the proposed method, several case studies are presented and simulation results are compared with those generated by genetic and Sequential Quadratic Programming (SQP) approaches

Transmission Expansion Planning in Deregulated Power Systems

The main goal of this thesis is to present a centralized static approach for transmission expansion planning in deregulated power systems. Restructuring and deregulation have unbundled the roles of network stakeholders. They exposed transmission planner to the new objectives and uncertainties. Unbundling the roles has brought new challenges for stakeholders. In these environments, stakeholders have different desires and expectations from the performance and expansion of the system. Therefore, new incentives and disincentives have emerged regarding transmission expansion decisions. This research work is involving with considering new objectives and uncertainties in transmission expansion planning. This research work is handled in six main parts. In the first part a probabilistic tool is presented for analyzing the performance of electric markets. In this part probability density function of locational marginal prices are computed for analysis electric market. The approach was applied to an 8-bus network. The effects of load curtailment and wheeling power on nodal prices were studied. The study shows wheeling transactions affect the locational marginal prices of the control area which transmit through them. It also shows that making wheeling transaction in proper directions can reduce the transmission congestion and postpone transmission expansion. In the second part, two market based criteria are presented to measure how much an expansion plan facilitates and promotes competition. The criteria are “average congestion cost” and “weighted standard deviation of mean of locational marginal prices”. Different weights are used in order to provide a competitive environment for more power system participants. Justification of costs is very important in competitive environments. Therefore the presented criteria are extended in order to consider transmission expansion costs. In the third part of the work, a transmission expansion planning approach is presented for deregulated environments. This approach consists of scenario technique and probabilistic optimal power flow which was presented in the first part. Scenario technique is used to take into account the non-random uncertainties. Probabilistic optimal power flow is used to consider the random uncertainties. The approach uses the market based criteria to measure the goodness of expansion plans. Market based criteria provide a non-discriminatory competitive environment for stakeholders. Minimax regret criterion is used in scenario technique for risk assessment and selecting the final plan. To determine which criterion leads to zero congestion cost and flat price profile at minimum cost or at minimum number of expansion plans, the presented approach was applied on IEEE 30 bus test system. The conventional risk assessment has some drawbacks. In the fourth part, drawbacks of scenario technique criteria are pointed out. New criteria are defined for the scenario technique. Fuzzy multi criteria decision making is used for the risk assessment of solutions. In this method a fuzzy appropriateness index is defined for selecting the final plan. The fuzzy appropriateness index is computed by aggregation of importance degrees of decision criteria and appropriateness degrees of expansion plans versus decision criteria. The presented approach is applied to IEEE 30 bus test system. The result was compared with conventional risk assessment in different cases. The comparison shows that fuzzy risk assessment overcomes the shortcomings of conventional risk assessment method. In the fifth part of the work, a transmission expansion planning approach with consideration given to stakeholders’ desires is presented. The approach considers the desires of demand customers, power producers, network owner(s), system operator, and regulator in transmission expansion planning. Stakeholders’ desires can be sought in competition, reliability, flexibility, network charge and environmental impacts. Fuzzy decision making is used for taking into account the desires of all stakeholders. A fuzzy appropriateness index is defined for measuring the goodness of expansion plans. The appropriateness index is defined by aggregating importance weights of stakeholders in decision making, importance degrees of stakeholders’ desires from the viewpoint of different stakeholders, and appropriateness degrees of expansion plans versus stakeholders’ desires. The approach was applied to IEEE 30 bus test systems to find the plan which compromise between stakeholders’ desires. The presented approach in the fifth part can not consider non-random uncertainties. In the sixth part, the presented approach is extended to consider stakeholders’ desires under non-random uncertainties. Fuzzy appropriateness index is defined to measure the goodness of each expansion plan in each scenario with considering the stakeholders’ desires. Fuzzy regret is defined with considering the occurrence degrees of future scenarios. Fuzzy regret of plan k in scenario l is equal to difference between the fuzzy appropriateness index of plan k in scenario l and fuzzy appropriateness index of optimal plan of scenario l. Fuzzy risk assessment is used to find the final plan. The steps of planning were described in details by applying the approach to an eight bus system. The following results were obtained from the simulation. The criteria “average congestion cost” and “weighted standard deviation of mean of locational marginal prices” with the weight “sum of mean of generation and load” are the best criteria for providing a competitive electric market. “Average congestion cost” is more insensitive that other criteria to the occurrence degrees of future scenarios. Fuzzy risk assessment overcomes the shortcomings of conventional risk assessment method. The presented approach selects the final plan by compromising between stakeholders’ desires