A NEW AGENT MATCHING SCHEME USING AN ORDERED FUZZY SIMILARITY MEASURE AND GAME THEORY

In this paper, an agent matching method for bilateral contracts in a multi-agent market is proposed. Each agent has a hierarchical representation of its trading commodity attributes by a tree structure of fuzzy attributes. Using this structure, the similarity between the trees of each pair of buyer and seller is computed using a new ordered fuzzy similarity algorithm. Then, using the concept of Stackelberg equilibrium in a leader–follower game, matchmaking is performed among the sellers and buyers. The fuzzy similarities of each agent with others in its personal viewpoint have been used as its payoffs in a bimatrix game. Through a case study for bilateral contracts of energy, the capabilities of the proposed agent-based system are illustrated.     

Decision making in dynamic stochastic Cournot games

In this paper, the Cournot competition is modeled as a stochastic dynamic game. In the proposed model, a stochastic market price function and stochastic dynamic decision functions of the rivals are considered. Since the optimal decision of a player needs the estimation of the unknown parameters of the market and rivals’ decisions, a combined estimation-optimization algorithm for decision making is proposed. The history of the rivals’ output quantities (supplies) and the market clearing price (MCP) are the only available information to the players. The convergence of the algorithm (for both estimation and decision making processes) is discussed. In addition, the stability conditions of the equilibrium points are analyzed using the converse Lyapunov theorem. Through the case studies, which are performed based on the California Independent System Operator (CA-ISO) historical public data, the theoretical results and the applicability of the proposed method are verified. Moreover, a comparative study among the agents using the proposed method, naïve expectation and adaptive expectation in the market is performed to show the effectiveness and applicability of the proposed method.     

SIMULTANEOUS STATE ESTIMATION AND LEARNING IN REPEATED COURNOT GAMES

The aim of this article is to propose that an intelligent agent can be able to decide properly in an incomplete information repeated Cournot game. The market model and the competitors’ decision models are not known to the players. The proposed agent employs a combination of the k-nearest neighbor (KNN) method and the Bayes classifier to predict the next action of its rivals, using the market decision history. The agent takes the predicted actions as an estimate of its next state and learns the expected payoff of its state-action pairs interactively using the reinforcement learning (RL) algorithm. The results of the proposed agent's competition with two benchmark competitors in different simulated Cournot games are presented. The simulation results show that the proposed agent can significantly earn more payoffs in comparison with the two benchmark agents.     

Uncertainty based distributed generation expansion planning in electricity markets

Decision making under uncertainty is a difficult task in most areas. The distributed generation expansion planning is certainly one of the problems where it is difficult to find an optimal solution for, due to the existence of various uncertain parameters that affect the optimal solution. This paper presents a distributed generation planning strategy based on the multi-objective decision making (MODM) approach under uncertainties. The proposed strategy achieves a Pareto set of locations, sizes, and typical types, for new DG installations in a case-study power network (as the result of a multi-objective optimization). The technical constraints of DG units are also considered in the MODM process. The effects of the uncertain parameters on choosing the best plan(s) are considered using robustness, flexibility, and risk exposure attributes. This aim is performed based on a composite index which is weighted sum of the three mentioned attributes. This paper considers four categories of uncertain parameters including the peak loading factor, electricity market price, and investment and operating costs for the DG long-term planning.     

An agent-based system for bilateral contracts of energy

In this paper, an agent-based system for bilateral contracts of energy is proposed. The generating companies submit their offers to the demand companies. The demand companies also submit their bids to the generators. Each load or generator’s agent wants to match with an opponent, which offers the most valuable proposal. However, the problem of simultaneous decision-making causes decision conflicts among the agents. To overcome this conflict, we assume loads as the leaders and generators as the followers. We use Stackelberg game to match the seller and buyer agents. The negotiation process between a buyer and its potential seller will determine the power price between them. This process is carried out through a proposed combined time-behavioral protocol (TBP). With negligible changes in around the agreed price, this protocol can reduce the negotiation time considerably. After successful negotiation, the seller and buyer agents could sign a bilateral contract of energy if the market conditions allow it. The applicability of the proposed method is illustrated through a case study.     

A dynamic replicator model of the players’ bids in an oligopolistic electricity market

In this paper, the replicator dynamics of the power suppliers’ bids in an oligopolistic electricity market are derived for both the fixed and variable demand cases. The replicator dynamics stability analysis is also performed. The dynamics of the electricity markets are the results of players’ decisions. The physical parameters of the power systems (such as the lines capacities, voltage limitations, etc.) also affect the market dynamics indirectly, through the changes in players’ behaviors. Assuming rational players, an optimal bidding strategy for constructing the supply function (SF) of a generating firm is presented and based on that, the dynamics of the bid replicators are studied. Both fixed demands and price sensitive demands are taken into account. The replicator model is presented in the well-known state space structure. A case study is presented to show the applicability of the developed dynamic replicator bid model, and also to show how the Nash–SFE equilibrium evolves over time.     

A hierarchical decision making model for the prioritization of distributed generation technologies: A case study for Iran

The purpose of this paper is to present an assessment and evaluation model for the prioritization of distributed generation (DG) technologies, both conventional and renewable, to meet the increasing load due to the growth rate in Iran, while considering the issue of sustainable development. The proposed hierarchical decision making strategy is presented from the viewpoint of either the distribution company (DisCo) or the independent power producer (IPP) as a private entity. Nowadays, DG is a broadly-used term that covers various technologies; however, it is difficult to find a unique DG technology that takes into account multiple considerations, such as economic, technical, and environmental attributes. For this purpose, a multi-attribute decision making (MADM) approach is used to assess the alternatives for DG technology with respect to their economic, technical and environmental attributes. In addition, a regional primary energy attribute is also included in the hierarchy to express the potential of various kinds of energy resources in the regions under study. The obtained priority of DG technologies help decision maker in each region how allocate their total investment budget to the various technologies. From the performed analysis, it is observed that gas turbines are almost the best technologies for investing in various regions of Iran. At the end of the decision making process, a sensitivity analysis is performed based on the state regulations to indicate how the variations of the attributes’ weights influence the DG alternatives’ priority. This proposed analytical framework is implemented in seven parts of Iran with different climatic conditions and energy resources.     

Cournot games with linear regression expectations in oligopolistic markets

In this paper, a Cournot game in an oligopolistic market with incomplete information is considered. The market consists of some producers that compete for getting higher payoffs. For optimal decision making, each player needs to estimate its rivals’ behaviors. This estimation is carried out using linear regression and recursive weighted least-squares method. As the information of each player about its rivals increases during the game, its estimation of their reaction functions becomes more accurate. Here, it is shown that by choosing appropriate regressors for estimating the strategies of other players at each time-step of the market and using them for making the next step decision, the game will converge to its Nash equilibrium point. The simulation results for an oligopolistic market show the effectiveness of the proposed method.