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    Simulation of Large Customer Price Response Under Time-of-Use Electricity Pricing Based on Multi-Agent System
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    Abstract:
    The mechanism of customer TOU response is the key for success of time-of-use pricing. However different customers have different price response behavior and no simple analytic model is possible for it. Therefore multi-agents simulation is introduced. In simulation research TOU price response is taken as a decentralized decision process of all related customers, while power supply corporation can dynamically adjust its policy according to customer response. The paper discusses in details with the design principle and framework of the simulation system. In the system customer agent is implemented as responsive agent with its response rules built on fuzzy logic, utility agent is implemented as learning agent with classifier learning algorithm as its learning rules, and environment management module manages the interaction among them. Then the simulation flow is given and finally a case study based on a real power supply corporation is given and simulation results reported.
    Keywords:
    Demand Response
    Corporation
    Demand-responsive electric loads respond to prices or other signals sent by the electric utility or power system operator by decreasing consumption (shedding) and/or shifting load in time. Demand response provides a variety of technical and economic benefits to the grid, usually causing minimal inconvenience to consumers. Traditionally, demand response has been used to decrease system-wide peak consumption. In that case, demand response events are called rarely and seek to achieve large changes in load. Today, demand response is also used to provide a variety of other services to power systems. In some cases, loads are expected to respond continuously while still meeting the needs of consumers. In this article, we describe the types of loads well-suited to demand response and their response methods, typical demand response program designs and control methods, and emerging applications of demand response.
    Demand Response
    Load management
    Peak demand
    Dynamic demand
    On demand
    Demand patterns
    Consumption
    Response time
    Load shifting
    Market demand schedule
    Derived demand
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    Demand Response
    Market demand schedule
    Load management
    On demand
    Demand patterns
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    Demand-responsive electric loads respond to prices or other signals sent by the electric utility or power system operator by decreasing consumption (shedding) and/or shifting load in time. Demand response provides a variety of technical and economic benefits to the grid, usually causing minimal inconvenience to consumers. Traditionally, demand response has been used to decrease system-wide peak consumption. In that case, demand response events are called rarely and seek to achieve large changes in load. Today, demand response is also used to provide a variety of other services to power systems. In some cases, loads are expected to respond continuously while still meeting the needs of consumers. In this article, we describe the types of loads well-suited to demand response and their response methods, typical demand response program designs and control methods, and emerging applications of demand response.
    Demand Response
    Load management
    Peak demand
    Dynamic demand
    On demand
    Consumption
    Response time
    Load shifting
    Demand patterns
    Market demand schedule
    Derived demand
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    Demand Response
    Response time
    Load management
    On demand
    Derived demand
    Market demand schedule
    Peak demand
    Dynamic demand
    Demand patterns
    Dynamic Pricing
    Citations (29)
    During an emergency due to a shortage of power, a load aggregator (LA) can use the demand response operation system to deploy demand response resources (DRRs) through various demand response (DR) programs. This paper introduces the demand response operation system for a load aggregator to manage various demand response resources in a smart grid environment.
    Demand Response
    News aggregator
    Load management
    Response time
    Economic shortage
    On demand
    Load shifting
    Emergency Response
    Peak demand
    Dynamic demand
    Power demand
    Demand patterns
    본 논문에서는 기존의 가입자가 직접 전력량을 조절하는 수동수요반응과는 달리 정해진 시간에 자동적으로 전력량 소모를 줄이는 자동수요반응 시스템을 제안하였다. 제안된 시스템은 SEP 2.0을 기반으로 동작하며 수요반응 관리 프로그램과 수요반응 서버, 수요반응 클라이언트로 구성된다. 수요반응 관리 프로그램은 가입자에게 전력사용현황을 알려주고 관리자에게는 수요반응 이벤트를 생성 및 취소할 수 있게 한다. 수요반응 서버는 수요반응 관리 프로그램에서 수신한 수요반응 이벤트를 SEP 2.0을 통해 수요반응 클라이언트로 전송하고 수요반응 클라이언트가 주기적으로 전송하는 미터링 데이터를 데이터베이스에 저장한다. 그리고 수요반응 클라이언트는 수신한 수요반응 이벤트에서 수요반응 이벤트 시작시간과 지속시간, 감소레벨을 추출한 뒤, 특정 기간 동안 전력소모를 감소시킨다. In this paper, we propose the automatic demand response systems which reduce the electric power consumption for the period automatically distinct from the existing passive demand response that a subscriber directly controls the energy consumption. The proposed systems are based on SEP 2.0 and consist of the demand response management program, the demand response server, and the demand response client. The demand response program shows the current status of the electric power use to a subscriber and supports the function which the administrator enables to creates or cancels a demand response event. The demand response server transmits the demand response event received from the demand response management program to the demand response client through SEP 2.0 protocol, and it stores the metering data from the demand response client in a database. After extracting the data, such as the demand response the start time, the duration, the reduction level, the demand response client reduces the electric power consumption for the period.
    Demand Response
    Peak demand
    Response time
    Load management
    On demand
    Demand patterns