A Game Theoretical Approach to Modeling Energy Consumption with Consumer Preference

Abstract-We propose a new game theoretical equilibrium model to analyze residential users' electricity consumption behavior in smart grid where energy usage and price data are exchanged between users and utilities via advanced communication. Consideration is given to users' possible preference on convenience over cost-saving under the real-time pricing in smart grid, and each user is assumed to have a preferred time window for using a particular appliance. As a result, each user (player) in the proposed energy consumption game wishes to maximize a payoff or utility consisting of two parts: the negative of electricity cost and the convenience of using appliances during their preferred time windows. Extensive numerical tests suggest that users with less flexibility on their preferred usage times have larger impact on the system performance at equilibrium. This provide insights for utilities to design their pricing based demand response schemes

Decomposition-Based Exact Algorithms For Risk-Constrained Traveling Salesman Problems With Discrete Random Arc Costs

Recently increasing attentions have been given to uncertainty handling in network optimization research. Along this trend, this paper discusses traveling salesman problem with discrete random arc costs while incorporating risk constraints. Minimizing expected total cost might not be enough because total costs of some realizations of the random arc costs might exceed the resource limit. To this respect, this paper presents a model of the traveling salesman problem that incorporates risk constraints based on Conditional Value at Risk to evaluate those worst-cost scenarios. Exact solution methods are developed and applied on the risk-constrained traveling salesman problem. Numerical experiments are conducted, and the results show the ability of the proposed methods in reducing the computational complexity

A Multistage Decision-Dependent Stochastic Bilevel Programming Approach For Power Generation Investment Expansion Planning

In this article, we study the long-term power generation investment expansion planning problem under uncertainty. We propose a bilevel optimization model that includes an upper-level multistage stochastic expansion planning problem and a collection of lower-level economic dispatch problems. This model seeks for the optimal sizing and siting for both thermal and wind power units to be built to maximize the expected profit for a profit-oriented power generation investor. To address the future uncertainties in the decision-making process, this article employs a decision-dependent stochastic programming approach. In the scenario tree, we calculate the non-stationary transition probabilities based on discrete choice theory and the economies of scale theory in electricity systems. The model is further reformulated as a single-level optimization problem and solved by decomposition algorithms. The investment decisions, computation times, and optimality of the decision-dependent model are evaluated by case studies on IEEE reliability test systems. The results show that the proposed decision-dependent model provides effective investment plans for long-term power generation expansion planning

Two-Stage Stochastic Unit Commitment Model Including Non-Generation Resources With Conditional Value-At-Risk Constraints

This paper presents a two-stage stochastic unit commitment (UC) model, which integrates non-generation resources such as demand response (DR) and energy storage (ES) while including risk constraints to balance between cost and system reliability due to the fluctuation of variable generation such as wind and solar power. This paper uses conditional value-at-risk (CVaR) measures to model risks associated with the decisions in a stochastic environment. In contrast to chance-constrained models requiring extra binary variables, risk constraints based on CVaR only involve linear constraints and continuous variables, making it more computationally attractive. The proposed models with risk constraints are able to avoid over-conservative solutions but still ensure system reliability represented by loss of loads. Then numerical experiments are conducted to study the effects of non-generation resources on generator schedules and the difference of total expected generation costs with risk consideration. Sensitivity analysis based on reliability parameters is also performed to test the decision preferences of confidence levels and load-shedding loss allowances on generation cost reduction. © 2014 Elsevier B.V

A Game Theoretical Approach To Modeling Energy Consumption With Consumer Preference

We propose a new game theoretical equilibrium model to analyze residential users\u27 electricity consumption behavior in smart grid where energy usage and price data are exchanged between users and utilities via advanced communication. Consideration is given to users\u27 possible preference on convenience over cost-saving under the real-time pricing in smart grid, and each user is assumed to have a preferred time window for using a particular appliance. As a result, each user (player) in the proposed energy consumption game wishes to maximize a payoff or utility consisting of two parts: the negative of electricity cost and the convenience of using appliances during their preferred time windows. Extensive numerical tests suggest that users with less flexibility on their preferred usage times have larger impact on the system performance at equilibrium. This provide insights for utilities to design their pricing based demand response schemes

A Quasi Exact Solution Approach For Scheduling Enhanced Coal Bed Methane Production Through Co2 Injection

The purpose of this research is to provide an easily accessible, effective and efficient solution method for the profit-maximization scheduling problem for CO2-enhanced coal bed methane (ECBM) production. ECBM has been a mature technology which can further extend the value of the unminable coal mines. The total profit of the production is based on the revenue generated by the methane produced as well as CO2 credits earned from the carbon market, and the production cost of the methane along with the CO2 operational cost. This scheduling problem is formulated as a nonlinear optimization program which includes bilinear terms in the constraints due to the modeling of physical reactions. In this paper, we use a quasi exact solution technique to solve the problem, where bilinear terms are eliminated by a discretization and linearization procedure. The original problem is then transformed to a mixed integer linear program with binary variables representing the discretization of the continuous fractional variables involved in those nonlinear constraints. This approach is yielding an equivalent program when enough binary variables are used. Numerical experiments show that this method can be easily implemented and yield almost exact solutions in reasonable computational times

Techno-Economic Analysis And Optimization Models For Carbon Capture And Storage: A Survey

Carbon dioxide (CO2) emissions are projected to increase significantly during the coming decades if effective environmental policies are not implemented, and the negative impacts of carbon emissions will eventually hinder economic and human development. Carbon capture and storage is proposed to mitigate the global climate change due to the increased concentration of carbon dioxide in the atmosphere. In this article, we focus on the technical developments and economic analysis of carbon capture and storage using optimization models and algorithms. The three main components of carbon capture and storage we discuss are: carbon capture, carbon dioxide transportation and carbon sequestration. In addition, to fulfill carbon dioxide reduction requirements, we also discuss the use ofmathematical programming models solving energy expansion planning, CO2 network design problems and CO 2 storage problems. Through the combination of technical and economic analysis of carbon capture and storage technologies, possible directions for sustainable developments of low-carbon energy economy can be evaluated. © Springer-Verlag Berlin Heidelberg 2013