Shisheng Huang

Electricity Cost Minimization for a Microgrid With Distributed Energy Resource Under Different Information Availability

In this paper, the electricity cost minimization problem is considered for a residential microgrid which consists of multiple households (users) equipped with renewable-based distributed energy resource (DER). Each user has a set of nonshiftable and shiftable loads. Bidirectional electricity transactions are allowed, and a dynamic pricing model for the purchasing/selling of electricity from/to the grid is proposed. In order to reduce the electricity cost, the following decisions needed to be made: 1) scheduling decisions for the shiftable loads; 2) purchasing/selling decisions for each user at each time slot; and 3) amount decisions of the electricity purchased/sold by the users. An optimization problem to minimize the total electricity cost is formulated to obtain the optimal amount of electricity consumed, sold, and purchased for each user, respectively. A centralized algorithm based on dynamic programming, Q-learning, and Lyapunov methods are proposed to solve the optimization problem with perfect information, with partial information, and without information of any time-varying parameters, respectively. For the latter two cases, distributed algorithms are designed for practical implementation. Simulation results show that the proposed schemes can provide effective management for household electricity usage and bidirectional transactions.

Cost Minimization of Charging Stations With Photovoltaics: An Approach With EV Classification

This paper proposes a novel electric vehicle (EV) classification scheme for a photovoltaic (PV)-powered EV charging station (CS) that reduces the effect of intermittency of electricity supply and the cost of energy trading of the CS. Since not all EV drivers would like to be environmentally friendly, all vehicles in the CS are divided into three categories: 1) premium; 2) conservative; and 3) green, according to their charging behavior. Premium and conservative EVs are considered interested only in charging their batteries, with noticeably higher rates of charging for premium EVs. Green vehicles are more environmentally friendly and thus assist the CS to reduce its cost of energy trading by allowing the CS to use their batteries as distributed storage. A different charging scheme is proposed for each type of EV, which is adopted by the CS to encourage more EVs to be green. A basic mixed-integer programming (MIP) technique is used to facilitate the proposed classification scheme. It is shown that the uncertainty in PV generation can be effectively compensated, along with minimization of total cost of energy trading to the CS, by consolidating more green EVs. Real solar and pricing data are used for performance analysis of the system. It is demonstrated that the total cost to the CS reduces considerably as the percentage of green vehicles increases and that the contributions of green EVs in winter are greater than those in summer.

The Effects of Vehicle-to-Grid Systems on Wind Power Integration in California

Abstract Renewable energy portfolio standards have already caused a large increase in the amount of electricity produced from renewable sources and this amount is expected to increase as the policy target dates draw closer. One technology that has benefited greatly from these standards is wind energy. The uncertainty inherent in wind electricity production dictates that nearly equal amounts of conventional generation resources be kept in reserve should wind electricity output should suddenly dip. The introduction of plug-in hybrid electric vehicles into the personal transportation fleet presents an interesting possible solution to this problem through the concept of vehicle-to-grid power. The ability of these vehicles to increase the wind production fraction for the California market when high levels of wind energy are present in the supply portfolio is examined.

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.

A multi-paradigm modeling framework for energy systems simulation and analysis

Abstract The modern world energy system is highly complex and interconnected and the effects of energy policies may have unintended consequences. Modeling and analysis tools can therefore be crucial to gaining insight into the interactions between system components and formulating policies that will shape the future energy system. We present in this work a multi-paradigm modeling framework that allows for the continual adjustment and refinement of energy system models as the understanding of the system under study increases. This flexible and open framework allows for the consideration of different levels of model aggregation, timescales and geographic considerations within the same model through the use of different modeling formalisms. We also present a case study of the combined California natural gas and electricity systems that illustrates how the framework may be used to account for the significant uncertainty that exists within the system.

Quantifying System-Level Benefits from Distributed Solar and Energy Storage

AbstractMicrogeneration using solar photovoltaic (PV) systems is one of the fastest growing applications of solar energy in the United States. Its success has been partly fueled by the availability of net metering by electric utilities. However, with increasing solar PV penetration, the availability of net metering is likely to be capped. Households would then need to rely on distributed storage to capture the full benefits of their installed PV systems. Although studies of these storage systems to assess their benefits to the individual household have been examined in literature, the systemwide benefits have yet to be fully examined. In this study, the utility level benefits of distributed PV systems coupled with electricity storage are quantified. The goal is to provide an estimate of these benefits so that these savings can potentially be translated into incentives to drive more PV investment. An agent-based residential electricity demand model is combined with a stochastic programming unit commitment ...

Electricity cost minimization for a residential smart Grid with distributed generation and bidirectional power transactions

In this paper, we consider the electricity cost minimization problem in a residential network where each community is equipped with a distributed power generation source and every household in the community has a set of essential and shiftable power demands. We allow bi-directional power transactions and assume a two-tier pricing model for the buying and selling of electricity from the grid. In this situation, in order to reduce the cost of electricity we are required to make, 1) Scheduling decisions for the shiftable demands, 2) The decisions on the amount of energy purchased from the gird by the users, 3) The decisions on the amount of energy sold to the grid by the users. We formulate a global centralized optimization problem and obtain the optimal amount of electricity consumed, sold and purchased for each household, respectively by assuming the availability of all current and future values of time-varying parameters. In reality, the lack of perfect information hampers the implementation of such global centralized optimization. Hence, we propose a distributed online algorithm which only requires the current values of the time-varying supply and demand processes. We then compare and determine the tradeoff between both formulations. Simulation results show that the proposed schemes can provide effective management for household electricity usage.

Smart Grid Testbed for Demand Focused Energy Management in End User Environments

Successful deployment of smart grids necessitates experimental validation of their state-of-theart designs in two-way communications, real-time demand response, and monitoring of consumers’ energy usage behavior. The objective is to observe consumers’ energy usage pattern and exploit this information to assist the grid in designing incentives, energy management mechanisms, and real-time demand response protocols, so as help the grid achieve lower costs and improve energy supply stability. Further, by feeding the observed information back to the consumers instantaneously, it is also possible to promote energy efficient behavior among users. To this end, this paper performs a literature survey on smart grid testbeds around the world, and presents the main accomplishments toward realizing a smart grid testbed at the Singapore University of Technology and Design (SUTD). The testbed is able to monitor, analyze, and evaluate smart grid communication network design and control mechanisms, and test the suitability of various communications networks for both residential and commercial buildings. The testbeds are deployed within the SUTD student dormitories and the main university campus to monitor and record end-user energy consumption in real-time, which will enable us to design incentives, control algorithms, and real-time demand response schemes. The testbed also provides an effective channel to evaluate the needs of communication networks to support various smart grid applications. In addition, our initial results demonstrate that our testbed can provide an effective platform to identify energy waste, and prompt the needs of a secure communications channel as the energy usage pattern can provide privacy related information on individual users.

Demand response management for power throttling air conditioning loads in residential Smart Grids

In this paper we develop an algorithm for peak load reduction to reduce the impact of increased air conditioner usage in a residential smart grid community. We develop Demand Response Management (DRM) plans that clearly spell out the maximum duration as well as maximum severity of inconvenience. We model the air conditioner as a power throttling device and for any given DRM plan we study the impact of increasing the number of power states on the resulting peak load reduction. Through simulations, we find out that adding just one additional state to the basic ON/OFF model, which can throttle power to 50% of the rated air conditioner power, can result in significant amount of peak reduction. However, the peak load that can be reduced is diminishing with the increase in number of states. Furthermore, we also observe the impact of inconvenience duration and inconvenience severity in terms of peak load reduction. These observations can serve as useful guidelines for developing appropriate DRM plans.

Minimizing commercial building cost in Smart Grid: An optimal meeting scheduling approach

We develop a mathematical programming approach to schedule meetings in an organization over a fixed period of time, while minimizing the wasted energy and possibly achieving more balanced demand distribution. The problem is formulated as a mixed integer linear program subject to a set of realistic constraints including peoples available time slots and energy consumption characteristics of the meeting rooms. Two objective functions are considered: minimizing the total energy used and minimizing the total energy cost. Our simulations illustrate that using the optimal schedule may result in significant savings, both economical and environmental.