The Power Matchmaking Transaction Based on Generation Algorithm Optimization

2013 ◽  
Vol 848 ◽  
pp. 161-165
Author(s):  
Yin Fang ◽  
Chao Ma ◽  
Yong Dai ◽  
Ying Ying Zhai ◽  
Zhen Liao Lv
Keyword(s):  
Social Welfare ◽  
Electricity Market ◽  
Power Market ◽  
Transformation Model ◽  
Power Transformation ◽  
Algorithm Optimization ◽  
Generation Algorithm ◽  
Welfare Maximization ◽  
Social Welfare Maximization ◽  
The Social

In regional electricity market environment, power market matchmaking transaction mechanism is used widely. The power transformation model of competitive integration market is built to meet the various needs, maintain the existing price system and make the social welfare maximization. The power matchmaking transaction based on generation algorithm is used in this paper to find a set of allocation, to maximize the search to the social welfare in numerous electrical distributions. For the purpose of minimizing the transaction risk, definitions of trust, an optimized matchmaking transaction based on generation algorithm are given in this paper.

scholarly journals Online Roommate Allocation Problem

2017 ◽  
Author(s):  
Guangda Huzhang ◽  
Xin Huang ◽  
Shengyu Zhang ◽  
Xiaohui Bei
Keyword(s):  
Social Welfare ◽  
Competitive Ratio ◽  
Allocation Problem ◽  
Market Model ◽  
Stability Conditions ◽  
Welfare Maximization ◽  
Negative Results ◽  
Social Welfare Maximization ◽  
The Social ◽  
Online Setting

We study the online allocation problem under a roommate market model introduced in [Chan et al., 2016]. Consider a fixed supply of n rooms and a list of 2n applicants arriving sequentially in an online fashion. The problem is to assign a room to each person upon her arrival, such that after the algorithm terminates, each room is shared by exactly two people. We focus on two objectives: (1) maximizing the social welfare, which is defined as the sum of valuations that applicants have for their rooms, plus the happiness value between each pair of roommates; (2) the allocation should satisfy certain stability conditions, such that no group of people would be willing to switch roommates or rooms. We first show a polynomial-time online algorithm that achieves constant competitive ratio for social welfare maximization. We then extend it to the case where each room is assigned to c > 2 people, and achieve a competitive ratio of Ω(1/c^2). Finally, we show both positive and negative results in satisfying different stability conditions in this online setting.

scholarly journals Game-Theory Modeling for Social Welfare Maximization in Smart Grids

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2315 ◽  
Author(s):  
Yu Hwang ◽  
Issac Sim ◽  
Young Sun ◽  
Heung-Jae Lee ◽  
Jin Kim
Keyword(s):  
Power Generation ◽  
Social Welfare ◽  
Smart Grids ◽  
Stackelberg Game ◽  
Evolutionary Game ◽  
Profit Maximization ◽  
Additional Parameter ◽  
Welfare Maximization ◽  
Social Welfare Maximization ◽  
Energy Strategies

In this paper, we study the Stackelberg game-based evolutionary game with two players, generators and energy users (EUs), for monetary profit maximization in real-time price (RTP) demand response (DR) systems. We propose two energy strategies, generator’s best-pricing and power-generation strategy and demand’s best electricity-usage strategy, which maximize the profit of generators and EUs, respectively, rather than maximizing the conventional unified profit of the generator and EUs. As a win–win strategy to reach the social-welfare maximization, the generators acquire the optimal power consumption calculated by the EUs, and the EUs obtain the optimal electricity price calculated by the generators to update their own energy parameters to achieve profit maximization over time, whenever the generators and the EUs execute their energy strategy in the proposed Stackelberg game structure. In the problem formulation, we newly formulate a generator profit function containing the additional parameter of the electricity usage of EUs to reflect the influence by the parameter. The simulation results show that the proposed energy strategies can effectively improve the profit of the generators to 45% compared to the beseline scheme, and reduce the electricity charge of the EUs by 15.6% on average. Furthermore, we confirmed the proposed algorithm can contribute to stabilization of power generation and peak-to-average ratio (PAR) reduction, which is one of the goals of DR.

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