Demand Response in Smart Grid: A Stackelberg Game-Theoretic Approach

Abstract

Smart grid is envisioned as the next generation of traditional electric power grid, which enables a two-way flow of electricity and information between power utility side and user side. Demand-response (DR) program is regarded as a promising solution for the future power grid by encouraging users to consume energy in a wise way so as to benefit both the power utility and users. In view of the requirements of devising efficient DR schemes for realizing smart grid, this dissertation develops two novel DR models under the framework of Stackelberg game theory, which are designed for the user side and power utility side respectively. First, a real-time price-based demand-response algorithm was proposed for achieving optimal load control of devices in a facility by forming a virtual electricity-trading process, where the Energy Management Center of the facility is the virtual retailer (leader) offering virtual retail prices, from which devices (followers) are supposed to purchase energy. A 1-leader, N-follower Stackelberg game is formulated to capture the interactions between them, and optimization problems are formed for each player to help select the optimal strategy. The existence of a unique Stackelberg equilibrium that provides optimal energy demands for each device was demonstrated. The simulation analysis showed that the Stackelberg game-based demand-response algorithm is effective for achieving the optimal load control of devices in response to real-time price changes with a trivial computation burden. Second, a novel demand-response model was presented for modeling the electricity trading process between one utility company (UC) and multiple users aimed at balancing supply and demand as well as flattening aggregated loads in the system. The interactions between the UC (leader) and users (followers) are formulated into a 1-leader, N-follower Stackelberg game, where minimization problems are defined for the utility company and each user with the target of minimizing the generation fluctuations and the costs of users. A pricing function is adopted for regulating real-time prices (RTP) at the utility company side, which then act as a coordinator, inducing users to join the game. An iterative algorithm is proposed to derive the Stackelberg equilibrium, through which optimal power generation and power demands are determined for the UC and users respectively. Numerical results indicate that the proposed method can efficiently reshape users’ demands, including flattening peak demands and filling the vacancy of valley demands, and significantly reduce the mismatch between supply and demand. |스마트 그리드에서 스타클버그 게임 이론을 이용한 수요반응 스마트 그리드는 전기사업소와 사용자간의 전력과 정보의 양방향 흐름을 실현하였고, 기존 전력 그리드의 차세대 기술로 발전 중이다. 수요관리 (DR) 프로그램은사용자들의 현명한 에너지 소비를 조장하며, 미래 파워 그리드의 유망한 해결책으로 간주된다. 효율적인 DR방식을 실현하기 위해, 본 논문에서는 스타클버그 게임 이론을 활용한 두 가지 DR 모델들을 소개한다. 첫째, 설비장비들을 조절하는데 사용되는 실시간 가격 기반 DR 알고리즘을 제안한다. 이 알고리즘은 가상의 전력교역과정을 구성한다. 해당 시설의 에너지관리센터는 가상의 중간상인(리더)으로써 에너지를 가상의 가격에 제공하며, 각 장비들(팔로어)은 제공되는 에너지를 구매한다. 1-리더, N-팔로어 스타클버그 게임은 이들간의 상호작용을 표현하였으며, 각 참가자들의 최적화 전략을 통해 최적문제들을 구성하였다. 각 장비에 대한 최적의 에너지 수요를 구하는 스타클버그 평형의 단일 해법을 구할 수 있음을 보였다. 시뮬레이션 분석은 스타클버그 게임 기반 DR 알고리즘의 효율성을 보여준다. 실시간 가격이 제공될 때, 각 장비들을 조작하는 최적의 수치들을 약간의 계산을 통해 구할 수 있다. 둘째, 한 개의 전기사업소(UC)와 다수의 사용자들 사이의 전력교역과정을 모델링하는 수요관리 프로그램을 제안한다. 이들의 목표는 수요와 공급의 균형을 맞추며, 전체 수요를 최소화하는 것이다. UC(리더)와 사용자들(팔로어) 사이의 상호작용은 1-리더, N-팔로어 스타클버그 게임을 통해 구성되었으며, 발전 변화량과 사용자들의 비용을 최소화 문제들은 UC와 각 사용자들을 위해 정의되었다. 전기사업소에서 가격 함수는 실시간 가격을 만들었으며, 이 함수는 사용자와의 조정자 역할을 한다. 스타클버그 평형을 찾을 수 있는 반복 알고리즘이 제안되었으며, 이를 통해 최적의 전력 생산과 수요가 결정된다. 제안하는 방식은 사용자의 수요를 변화시켜 최고수요가 낮아졌으며, 최저수요가 높아졌고, 수요와 공급 사이의 격차가 줄어들었음을 시뮬레이션 결과들을 통해 증명하였다.; Smart grid is envisioned as the next generation of traditional electric power grid, which enables a two-way flow of electricity and information between power utility side and user side. Demand-response (DR) program is regarded as a promising solution for the future power grid by encouraging users to consume energy in a wise way so as to benefit both the power utility and users. In view of the requirements of devising efficient DR schemes for realizing smart grid, this dissertation develops two novel DR models under the framework of Stackelberg game theory, which are designed for the user side and power utility side respectively. First, a real-time price-based demand-response algorithm was proposed for achieving optimal load control of devices in a facility by forming a virtual electricity-trading process, where the Energy Management Center of the facility is the virtual retailer (leader) offering virtual retail prices, from which devices (followers) are supposed to purchase energy. A 1-leader, N-follower Stackelberg game is formulated to capture the interactions between them, and optimization problems are formed for each player to help select the optimal strategy. The existence of a unique Stackelberg equilibrium that provides optimal energy demands for each device was demonstrated. The simulation analysis showed that the Stackelberg game-based demand-response algorithm is effective for achieving the optimal load control of devices in response to real-time price changes with a trivial computation burden. Second, a novel demand-response model was presented for modeling the electricity trading process between one utility company (UC) and multiple users aimed at balancing supply and demand as well as flattening aggregated loads in the system. The interactions between the UC (leader) and users (followers) are formulated into a 1-leader, N-follower Stackelberg game, where minimization problems are defined for the utility company and each user with the target of minimizing the generation fluctuations and the costs of users. A pricing function is adopted for regulating real-time prices (RTP) at the utility company side, which then act as a coordinator, inducing users to join the game. An iterative algorithm is proposed to derive the Stackelberg equilibrium, through which optimal power generation and power demands are determined for the UC and users respectively. Numerical results indicate that the proposed method can efficiently reshape users’ demands, including flattening peak demands and filling the vacancy of valley demands, and significantly reduce the mismatch between supply and demand.