Zaiyue Yang

Demand Response Management With Multiple Utility Companies: A Two-Level Game Approach

Demand Response Management (DRM) is a key component of the future smart grid that helps to reduce power peak load and variation. Different from most existing studies that focus on the scenario with a single utility company, this paper studies DRM with multiple utility companies. First, the interaction between utility companies and residential users is modeled as a two-level game. That is, the competition among the utility companies is formulated as a non-cooperative game, while the interaction among the residential users is formulated as an evolutionary game. Then, we prove that the proposed strategies are able to make both games converge to their own equilibrium. In addtion, the strategies for the utility companies and the residential users are implemented by distributed algorithms. Illustrative examples show that the proposed scheme is able to significantly reduce peak load and demand variation.

Residential Energy Consumption Scheduling: A Coupled-Constraint Game Approach

This paper investigates the residential energy consumption scheduling problem, which is formulated as a coupled-constraint game by taking the interaction among users and the temporally-coupled constraint into consideration. The proposed solution consists of two parts. Firstly, dual decomposition is applied to transform the original coupled-constraint game into a decoupled one. Then, Nash equilibrium of the decoupled game is proven to be achievable via best response, which is computed by gradient projection. The proposed solution is also extended to an online version, which is able to alleviate the impact of the price prediction error. Numerical results demonstrate that the proposed approach can effectively shift the peak-hour demand to off-peak hours, enhance the welfare of each user, and minimize the peak-to-average ratio. The scalability of the approach and the impact of the user number are also investigated.

Three-Party Energy Management With Distributed Energy Resources in Smart Grid

In this paper, the benefits of distributed energy resources are considered in an energy management scheme for a smart community consisting of a large number of residential units (RUs) and a shared facility controller (SFC). A noncooperative Stackelberg game between the RUs and the SFC is proposed in order to explore how both entities can benefit, in terms of achieved utility and minimizing total cost respectively, from their energy trading with each other and the grid. From the properties of the game, it is shown that the maximum benefit to the SFC, in terms of reduction in total cost, is obtained at the unique and strategy-proof Stackelberg equilibrium (SE). It is further shown that the SE is guaranteed to be reached by the SFC and RUs by executing the proposed algorithm in a distributed fashion, where participating RUs comply with their best strategies in response to the action chosen by the SFC. In addition, a charging-discharging scheme is introduced for the SFCs storage device that can further lower the SFCs total cost if the proposed game is implemented. Numerical experiments confirm the effectiveness of the proposed scheme.

Load scheduling with price uncertainty and temporally-coupled constraints in smart grids

Summary form only given. Recent years have witnessed the significant growth in electricity consumption. The emerging smart grid aims to address the ever-increasing load through appropriate scheduling, i.e., to shift the energy demand from peak to off-peak periods by pricing tariffs as incentives. Under the real-time pricing environment, due to the uncertainty of future prices, load scheduling is formulated as an optimization problem with expectation and temporally-coupled constraints. Instead of resorting to stochastic dynamic programming that is generally prohibitive to be explicitly solved, we propose dual decomposition and stochastic gradient to solve the problem. That is, the primal problem is firstly dually decomposed into a series of separable subproblems, and then the price uncertainty in each subproblem is addressed by stochastic gradient based on the statistical knowledge of future prices. In addition, we propose an online approach to further alleviate the impact of price prediction error. Numerical results are provided to validate our theoretical analysis.

Distributed Real-Time Demand Response in Multiseller–Multibuyer Smart Distribution Grid

Demand response is a key solution in smart grid to address the ever-increasing peak energy consumption. With multiple utility companies, users will decide from which utility company to buy electricity and how much to buy. Consequently, how to devise distributed real-time demand response in the multiseller-multibuyer environment emerges as a critical problem in future smart grid. In this paper, we focus on the real-time interactions among multiple utility companies and multiple users. We propose a distributed real-time demand response algorithm to determine each users demand and each utility companys supply simultaneously. By applying dual decomposition, the original problem is firstly decoupled into single-seller-multibuyer subsystems; then, the demand response problem in each subsystem can be distributively solved. The major advantage of this approach is that each utility company and user locally solve subproblems to perform energy allocation, instead of requiring a central controller or any third party. Therefore, privacy is guaranteed because no entity needs to reveal or exchange private information. Numerical results are presented to verify efficiency and effectiveness of the proposed approach.

Optimal Coordination of Mobile Sensors for Target Tracking Under Additive and Multiplicative Noises

In this paper, the target tracking problem is investigated for a tracking system with mobile range-only sensors. Being different from most previous studies, both additive and multiplicative noises in measurements are taken into consideration. An optimal coordination strategy, including sensor selection and sensor motion, is proposed to maximize the tracking accuracy. In particular, by fully utilizing the properties of objective function, the search space and variables of the original optimization problem can be significantly reduced. Based on this reduction, three algorithms are designed, respectively, for the following: 1) efficient selection of task sensors; 2) reduction on combinations of task sensors; and 3) efficient search of optimal sensor motion. The performance of the proposed coordination strategy is illustrated by simulations.

Energy Storage Sharing in Smart Grid: A Modified Auction-Based Approach

This paper studies the solution of joint energy storage (ES) ownership sharing between multiple shared facility controllers (SFCs) and those dwelling in a residential community. The main objective is to enable the residential units (RUs) to decide on the fraction of their ES capacity that they want to share with the SFCs of the community in order to assist them in storing electricity, e.g., for fulfilling the demand of various shared facilities. To this end, a modified auction-based mechanism is designed that captures the interaction between the SFCs and the RUs so as to determine the auction price and the allocation of ES shared by the RUs that governs the proposed joint ES ownership. The fraction of the capacity of the storage that each RU decides to put into the market to share with the SFCs and the auction price are determined by a noncooperative Stackelberg game formulated between the RUs and the auctioneer. It is shown that the proposed auction possesses the incentive compatibility and the individual rationality properties, which are leveraged via the unique Stackelberg equilibrium solution of the game. Numerical experiments are provided to confirm the effectiveness of the proposed scheme.

Optimal Cooperative Charging Strategy for a Smart Charging Station of Electric Vehicles

This paper proposes a novel cooperative charging strategy for a smart charging station in the dynamic electricity pricing environment, which helps electric vehicles (EVs) to economically accomplish the charging task by the given deadlines. This strategy allows EVs to share their battery-stored energy with each other under the coordination of an aggregator, so that more flexibility is given to the aggregator for better scheduling. Mathematically, the scheduling problem is formulated as a constrained mixed-integer linear program (MILP) to capture the discrete nature of the battery states, i.e., charging, idle and discharging. Then, an efficient algorithm is proposed to solve the MILP by means of dual decomposition and Benders decomposition. At last, the algorithm can be implemented in a distributed fashion, which makes it scalable and thus suitable for large-scale scheduling problems. Numerical results validate our theoretical analysis.

Profit Maximization for Plug-In Electric Taxi With Uncertain Future Electricity Prices

This paper investigates the optimal charging strategy for a plug-in electric taxi (PET) to maximize its operating profit by choosing proper charging slots, subject to uncertain electricity prices and time-varying incomes. As PET consumes more electricity and possesses different charging behaviors from the widely studied private electric vehicles, this problem deserves special treatment. First, in order to tackle the uncertain electricity prices, a simple thresholding method is proposed to determine the optimal charging slot, where the thresholds are computed via a backward induction algorithm. Then, the properties that reveal the insights of the algorithm are presented. Then, several practical factors are included in algorithm design to approach a more realistic solution, such as an accurate battery model, the additional power consumption of driving PET to charging station, and the battery loss during charging and discharging processes. Numerical results show that the proposed algorithm is able to improve the profit and significantly reduce the expense.

Optimal Charging Schedule for a Battery Switching Station Serving Electric Buses

We propose a model of a battery switching station (BSS) for electric buses (EBs) that captures the predictability of bus operation. We schedule battery charging in the BSS so that every EB arrives to find a battery ready for switching. We develop an efficient algorithm to compute an optimal schedule. It uses dual decomposition to decouple the charging decisions at different charging boxes so that independent subproblems can be solved in parallel at individual charging boxes, making the algorithm inherently scalable as the size of the BSS grows. We propose a direct projection method that solves these subproblems rapidly. Numerical results illustrate that the proposed approach is far more efficient and scalable than generic algorithms and existing solvers.