1,720,966 research outputs found
Estimation, Learning, and Stability Analysis of Supply Function Equilibrium Game for Generation Companies
No full textSupply Function Equilibrium Game with Myopic Adjustment and Adaptive Expectation
No full textIn this paper, the competition among supplier agents in an auction is modeled as a supply function equilibrium game. The strategy of each player is a function of price versus quantity. Each player wants to maximize a monetary payoff over the time-steps in a repeated game. It is assumed that the players have only access to the historical information of the rivals’ decisions. Therefore, the players need to estimate the decision of the rivals for the next step. A nonlinear dynamic gradient learning method, namely myopic adjustment, is proposed for decision making of the players which works together with an adaptive expectation method. It is shown that the game model admits a unique Nash equilibrium point. A sufficient condition for the convergence of the proposed method to the Nash equilibrium point is also derived and a region attraction of the proposed dynamical system is computed using Lyapunov’s second method
Decentralized charging coordination of plug-in electric vehicles based on reverse stackelberg game
No full textThis paper proposes a decentralized hierarchical price function design for charging coordination of Plug-in Electric Vehicles (PEVs) based on the reverse Stackelberg mechanism. We consider an aggregator who purchases energy from the wholesale energy market. The aggregator acts as the leader for a group of PEVs and determines the price of energy versus consumption at each hour a day as its decision function. In the followers level, the optimal charging strategies of the PEVs are coupled through the electricity price. The PEVs in a group are considered to cooperate in finding their Nash-Pareto-optimal charging strategy, by minimizing a social cost function. We propose a decentralized algorithm by combination of mean-filed control and reverse Stackelberg game to find an optimal linear price function while the followers' strategies converge to varepsilon{N}-Nash equilibrium point of the game
Decentralized Hierarchical Planning of PEVs Based on Mean-Field Reverse Stackelberg Game
No full textIn the reverse Stackelberg mechanism, by considering a decision function for the leader rather than a decision value in the conventional Stackelberg game, the leader can explore a wider decision space. This flexibility can result in realizing the globally optimal solution of the leader's objective function, while controlling the reaction function of the followers, simultaneously. We consider an aggregator who purchases energy from the wholesale energy market. The aggregator acts as the leader for a group of plugged in electric vehicles (PEVs) and determines the price of energy versus consumption at each hour a day as its decision function. In the followers level, since the optimal charging strategies of the PEVs are coupled through the electricity price, the PEVs in a group are considered to cooperate in finding their Nash-Pareto-optimal charging strategy, by minimizing a social cost function. For a large number of PEVs, the cooperative cost minimization of PEVs can be modeled as a cooperative mean-field (MF) game. We propose a decentralized MF optimal control algorithm and prove that the algorithm converges to leader-follower MF arepsilon _{N}-Nash equilibrium point of the game. Furthermore, a decentralized reverse Stackelberg algorithm is implemented to achieve the optimal linear price function of the leader. Simulation results and comparison with benchmark methods are performed to demonstrate the advantages of the proposed method. Note to Practitioners-The effect of a large population of PEVs on the power grid such as overload and voltage drop is inevitable. Motivated by this, there are many research articles which propose different centralized and decentralized charging coordination solutions to address this problem. The core idea in the most of literature is: 'How to IMPLEMENT a demand response (DR) program appropriately for charging coordination problem to avoid high peak load?' However, none of them propose a practical algorithm on 'How to DESIGN a DR optimally by having limited information from the clients?' We propose a bilevel optimization algorithm to both design a DR program (i.e., price function) and also implement DR program to flatten the demand curve, accordingly. Another advantage of the proposed method is that the information structure of the problem is close to reality. There is no information exchange among the clients and also the utility company does not need to know the private information of the clients. The Utility Company only knows the charging profile of the PEVs in each day and broadcasts the price signal to the clients for the next day. The method is illustrated in an IEEE 5-bus system that supports our claims
Game-theoretic demand side management of a neighbourhood of smart homes with real and virtual energy storage
No full textDemand side management strategies are used in many application scenarios in order to mitigate high peak demands and reduce them to comply with the availability of electricity, which has many implications such as, e.g., costs minimization and efficiency increase, among the others. In this paper, we present a game-theoretic approach for the demand side management of a neighbourhood of smart homes with real and virtual energy storage, where the neighborhood interacts with the energy retailer by means of a local coordinator. We model the strategic interaction among the smart homes as a non-cooperative aggregative game, so as to save communication bandwidth, computational burden and preserve data privacy. The key highlights of the study are: non-simultaneous charging and discharging of the battery using relaxed convex formulation and reduction in the peak-to-average ratio using game-theoretic DSM modelling
Decentralized Control of Residential Energy Storage System for Community Peak Shaving: A Constrained Aggregative Game
No full textDeregulation of the power network, along with integration of renewable energy resources and energy storage systems, anticipates an increased decision making autonomy to the end-users. Curtailing the peak, also known as peak shaving, is one such aspect where the end-users could play a significant role in making the grid more resilient and robust. In this regard, we consider a grid-connected community microgrid comprising of a number of residential households, each equipped with a PV panel and a battery. We formulate the peak shaving of the community microgrid as a non-cooperative aggregative game, where the allowable peak acts as the global constraint which has to be satisfied by the community. Further, we propose a decentralized control algorithm to achieve the equilibrium strategy of the aggregative game, known as the generalized Nash equilibrium. To validate the usefulness of the proposed approach, we carry out different case studies using real dataset and highlight the key findings of the study
Maximizing Revenue From Selfish Agents in Crowd Tasks: Indirect Incentive Strategies
No full textWe study mechanisms incentivizing the contribution of selfish agents addressing crowd tasks in a Bayesian setting with externalities due to network effects. An example is crowdsensing, where a large group of people share data collected by their mobile devices. The central problem we investigate is the relationship between direct and indirect mechanisms. Indeed, while direct mechanisms represent tools to address implementation problems optimally, the requirement that the contribution level of every agent when addressing the task is chosen by the mechanism makes these mechanisms hardly used in practice. On the other hand, while indirect mechanisms allow every agent to be free to choose their contribution level, these mechanisms may be highly inefficient. Our desideratum is to design indirect mechanisms that closely match the performance of optimal direct mechanisms. We design an indirect mechanism such that the Price of Stability over the revenue is bounded and in special cases without network effects the Price of Anarchy over the revenue is one. Our results suggest that indirect revelation mechanisms can be an excellent option in real-world applications
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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