Demand Response Cost Research Articles

Generation scheduling in smart grid environment using global best artificial bee colony algorithm

Generation scheduling is an important concern of the current power system which is suffering from many obstacles of limited generation resources, grown energy demand and fuel price, inconsistent load demand and fluctuations of available wind power in case of the thermal–wind system. Smart grid system has a great potential of tumbling existing power system difficulties with intelligent infrastructure and computation technologies. Three different distributed energy resources, namely, distributed generation, demand response and gridable vehicles are used in this paper to overcome the power system hitches. The classical generation scheduling is solved with insertion of the cost of demand response and the cost model pertaining to underestimation and overestimation of fluctuating wind power. The modified optimization problem is solved using an efficient Global best artificial bee colony algorithm for 10 generating units test system. Generation scheduling in the smart grid environment yields a significant reduction in the total cost.

Optimal combined scheduling of generation and demand response with demand resource constraints

Demand response (DR) extends customer participation to power systems and results in a paradigm shift from simplex to interactive operation in power systems due to the advancement of smart grid technology. Therefore, it is important to model the customer characteristics in DR. This paper proposes customer information as the registration and participation information of DR, thus providing indices for evaluating customer response, such as DR magnitude, duration, frequency and marginal cost. The customer response characteristics are modeled from this information. This paper also introduces the new concept of virtual generation resources, whose marginal costs are calculated in the same manner as conventional generation marginal costs, according to customer information. Finally, some of the DR constraints are manipulated and expressed using the information modeled in this paper with various status flags. Optimal scheduling, combined with generation and DR, is proposed by minimizing the system operation cost, including generation and DR costs, with the generation and DR constraints developed in this paper.

Avoided cost estimation and cost-effectiveness of permanent load shifting in California

Two intersecting events of the last two decades have transformed the electricity industry: (1) market restructure designed to introduce wholesale market competition; and (2) renewable energy development fostered by government policies, including renewable portfolio standards (RPS). There is, however, little research to answer the question: what is the avoided cost of demand response (DR) in a restructured market with RPS? This paper offers an answer to this question, which is critical for determining the cost-effectiveness of DR and its program design and implementation. We illustrate the answer’s usefulness with five real-world examples of permanent load shifting (PLS) in California. These examples show that a PLS system’s cost-effectiveness depends on its site-specific characteristics, including location, installation cost and performance, thus affirming the use of a targeted approach to DR design and implementation.

Demand response regulation modeling based on distribution system asset efficiency

In this paper, a new comprehensive model is represented to analyze the effect of demand response (DR) on the distribution system efficiency and the benefit of distribution companies (DISCOs). This paper defines the distribution system efficiency in view point of government and determines marginal cost of DR to realize governance efficiency. We use energy not supplied index to evaluate quality of services. The proposed model for the distribution system efficiency employs distribution indices called as efficiency indicators to calculate DISCOs’ benefit and governance efficiency. Using the proposed model, the impacts of DR on efficiency of distribution system and benefit of DISCOs are analyzed using graph theory. The new model can help regulators make optimal decisions regarding DR programs. A real distribution system with about 200,000 populations is used to study the impacts of implementing DR programs on efficiency indicators.

Transit Contracting Reexamined: Determinants of Cost Efficiency and Resource Allocation

National Transit Data from 1993 through 2004 is analyzed to examine the effects of contracting out on the cost efficiency and resource allocation of motor bus and demand response services. For motor bus service, results indicate no difference in cost between in-house and fully contracted operations. Contracting has a weak curvilinear association to total cost, suggesting that the most cost-efficient agencies either fully contract or they provide full in-house service. In contrast, demand response contracting is associated with about 20% lower total costs. Competitive conditions or the choice by agencies to offer specialized services may explain this result. Transit agencies that contract with multiple motor bus providers pay a cost premium, whereas multiple providers have no effect on demand response cost. Contracting does not affect the growth of cost for either service. An examination of expense subcategories reveals no reduction in administrative expenses when agencies contract services and only a partial reduction in nonoperational maintenance expenses. Overall, the results call into question the efficacy of competitive contracting models of transit service delivery and the use of fully allocated costing methods in make-versus-buy decisions.