Chenye Wu

Demand side management for Wind Power Integration in microgrid using dynamic potential game theory

We propose a novel demand side management method to tackle the intermittency in wind power generation. Our focus is on an isolated microgrid with one wind turbine, one fast-responding conventional generator, and several users. Users act as independent decision makers in shaping their own load profiles. Using dynamic potential game theory, we analyze and coordinate the interactions among users to efficiently utilize the available renewable and conventional energy resources to minimize the total energy cost in the system. We further model the inter-temporal variations of the available wind power as a Markov chain based on field data. Using techniques from dynamic potential game theory, we first derive closed-form expressions for the best responses for the users that participate in demand side management. Then, we investigate the efficiency of the constructed game model at the equilibrium. Finally, the system performance is assessed using computer simulation. In particular, our proposed scheme saves 38% generation cost compared with the case without demand side management.

Consensus + Innovations Approach for Distributed Multiagent Coordination in a Microgrid

Distributed energy resources and demand-side management are expected to become more prevalent in the future electric power system. Coordinating the increased number of grid participants in an efficient and reliable way is going to be a major challenge. A potential solution is the employment of a distributed energy management approach, which uses intelligence distributed over the grid to balance supply and demand. In this paper, we specifically consider the situation in which distributed resources and loads form microgrids within the bulk power system in which the load is supplied by local generation. A distributed energy management approach based on the consensus + innovations method is presented and used to coordinate local generation, flexible load, and storage devices within the microgrid. The approach takes advantage of the fact that, at the optimal allocation settings, the marginal costs given as a function of the power output/consumption need to be equal for all nonbinding network resources. Solutions for single time step, as well as multitime step optimization including intertemporal constraints, are presented.

Wind power integration via aggregator-consumer coordination: A game theoretic approach

Due to the stochastic nature of wind power, its large-scale integration into the power grid requires techniques to constantly balance the load with the time-varying supply. This can be done via smart scheduling of energy consumption and storage units among end users. In this paper, we propose a game-theoretic algorithm to be implemented in an aggregator in order to coordinate the operation of demand-side resources via pricing in order to tackle the intermittency and fluctuations in wind power generation. The demand-side resources to be considered are both non-shiftable and shiftable load, in particular, electric vehicles that charge or discharge their batteries to provide extra resource management flexibility. After formulating the interactions in an aggregator-consumer system as a game, we analytically prove the existence and uniqueness of the Nash equilibrium in the formulated game model. Simulation results show that our proposed design scheme can benefit both end users (in terms of reducing energy expenses) and the power grid (in terms of integrating wind power).

A Unifying Market Power Measure for Deregulated Transmission-Constrained Electricity Markets

Market power assessment is a prime concern when designing a deregulated electricity market. In this paper, we propose a new functional market power measure, termed transmission constrained network flow (TCNF), that unifies three large classes of transmission constrained structural market power indices in the literature: residual supply based, network flow based, and minimal generation based. Furthermore, it is suitable for demand-response and renewable integration and hence more amenable to identifying market power in the future smart grid. The measure is defined abstractly, and allows incorporation of power flow equations in multiple ways; we investigate the current market operations using a DC approximation and further explore the possibility of including detailed AC power flow models through semidefinite relaxation, and interior-point algorithms from Matpower. Finally, we provide extensive simulations on IEEE benchmark systems and highlight the complex interaction of engineering constraints with market power assessment.

PEV-based reactive power compensation for wind DG units: A stackelberg game approach

There has been a growing interest recently towards integrating more renewable energy resources, in particular wind power, in form of distributed generation (DG) units. However, one important challenge with wind DG units is to provide low-cost and fast-responding reactive power compensation of the wind turbines inductive load to ensure a stable voltage profile in the system. Since reactive power can only be compensated locally, we consider a scenario where a wind DG unit is co-located with a plug-in electric vehicle (PEV) charging station or a parking lot, and we investigate how to align incentives to encourage PEV owners to participate in reactive power compensation for wind DG units. For this purpose, in this paper, we introduce a two-stage Stackelberg game between the wind DG unit and the PEV owners. We use backward induction to analyze the formulated game and derive the optimal pricing scheme. We assess the performance of our proposed scheme using field data and make suggestions for the size of the charging stations.

Smart meter deployment optimization for efficient electrical appliance state monitoring

Monitoring the energy consumptions of the massive electrical appliances in buildings has attracted great attentions for smart, green and sustainable living. Traditional approaches generally require large-scale smart sensor/meter networks, and thus suffer from the high deployment, maintenance and data collection costs. In this paper, we propose methodologies and algorithms to optimize the smart meter deployments to track the on/off states of the massive electrical appliances by using the minimal number of smart meters. Particularly, based on the tree structure of the power distribution networks we show the deployment of m meters will decompose the power distribution network into a forest of m mono-meter trees. Each mono-meter tree has depth 1, with one meter at the root and a set of appliances at the leaves. A mono-meter tree is clear if the meter at the root can decode the on/off states of its leaves without error. Based on it, the smart meter deployment optimization problem is to optimize the meter deployment locations, so as to minimize the number of required meters while keeping all the mono-meter trees being clear. We prove this problem is NP-hard, and propose a greedy algorithm to approach it by utilizing the bounds of degree and the maximum power of the load tree. We show the greedy algorithm has at most 2 approximation ratio. Finally,we assess our approach in different structure power networks by simulations. The simulation results suggest a reasonable good performance of our proposed smart meter deployment strategy.

Dual-pricing policy for controller-side strategies in demand side management

Nowadays, smart grid is drawing great emphasis in the U.S., Europe and China with the increasing awareness of the notion - green world. A whole bunch of new smart grid related technologies and projects are booming throughout the world. These technologies fundamentally change the way how demand side management is done in the grid. In this paper, we propose a demand side management model, where appliances are categorized into non-interruptible tasks and interruptible tasks with parameters that are easy to implement in smart meters. Within our model, we adopt a pricing policy which leads to an optimal user-side strategy and analyze several strategies for the grid controller to set the future prices for the power grid in order to flatten the aggregated profile. A novel dual-pricing policy which sets different prices for different types of tasks is also proposed. Simulation results show that our designed mechanism can help reduce the peak-to-average ratio and coefficient of variation of the aggregated profile very well.

PEV-based P-Q control in line distribution networks with high requirement for reactive power compensation

While plug-in electric vehicles (PEVs) are expected to provide economic and environmental benefits to the transportation sector, they may also help the electric grid, both as a potential source of energy storage and as a means to improve power quality and reliability. In this paper, our focus is on the latter, where PEVs offer reactive power compensation using P-Q control at their charger inverters. In this regard, we develop a new optimization-based P-Q control strategy for PEV charging stations to be implemented in line distribution networks that are in great need of reactive power compensation, either because of serving large industrial loads or due to the inductive impact of distribution level wind turbines. Our design is based on a nonlinear power flow analysis, and the design objectives are to perform voltage regulation and demand response. Through various computer simulations, we assess our proposed PEV-based reactive power compensation and compare it with the case where no P-Q control is conducted at PEV charging stations.

A unifying approach to assessing market power in deregulated electricity markets

A competitive deregulated electricity market with increasingly active market players is foreseen to be the future of the electricity industry. In such settings, market power assessment is a primary concern. In this paper, we propose a novel functional approach for measuring long term market power that unifies a variety of popular market power indices. Specifically, the new measure, termed transmission constrained network flow (TCNF), unifies three large classes of market power measures: residual supply based, network flow based, and minimal generation based. Further, TCNF provides valuable information about market power not captured by prior indices. We derive its analytic properties and test its efficacy on IEEE test systems.

Enhanced secondary frequency control via distributed peer-to-peer communication

The prevalent distributed generation resources warrant reconsideration on how their coordination is achieved. In this paper, we particularly focus on how to enhance secondary frequency control by exploiting peer-to-peer communication among the resources. We design a control framework based on a consensus-plus-global-innovation approach. The control signals of distributed resources are updated in response to a global innovation (utilizing the area control error signal), and additional information exchanged via communication among neighboring resources. We show that such a distributed control scheme can be very well approximated by a proportional-integral controller and stabilizes the system. Moreover, since our scheme takes advantage of both the global and neighboring information, simulation results demonstrate that our scheme stabilizes the system significantly faster than the conventional automatic generation control framework. In addition, our control scheme asymptotically achieves the cost effectiveness.