Hamed Mohsenian-Rad

Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design

In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price into the customer side. In addition, smart pricing can be used to seek social benefits and to implement social objectives. To achieve social objectives, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. In this paper, we propose an efficient pricing method to tackle this problem. We assume that each user is equipped with an energy consumption controller (ECC) as part of its smart meter. All smart meters are connected to not only the power grid but also a communication infrastructure. This allows two-way communication among smart meters and the utility company. We analytically model each users preferences and energy consumption patterns in form of a utility function. Based on this model, we propose a Vickrey-Clarke-Groves (VCG) mechanism which aims to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each users electricity bill payment. Finally, we verify some important properties of our proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation results confirm that the proposed pricing method can benefit both users and utility companies.

Optimal Operation of Independent Storage Systems in Energy and Reserve Markets With High Wind Penetration

In this paper, we consider a scenario where a group of investor-owned independently-operated storage units seek to offer energy and reserve in the day-ahead market and energy in the hour-ahead market. We are particularly interested in the case where a significant portion of the power generated in the grid is from wind and other intermittent renewable energy resources. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. We show that the formulated stochastic program can be converted to a convex optimization problem to be solved efficiently. Our simulation results also show that our design can assure profitability of the private investment on storage units. We also investigate the impact of various design parameters, such as the size and location of the storage unit on increasing the profit.

False data injection attacks with incomplete information against smart power grids

False data injection attacks have recently been introduced as an important class of cyber attacks against smart grids wide area measurement and monitoring systems. These attacks aim to compromise the readings of multiple power grid sensors and phasor measurement units in order to mislead the operation and control centers. Recent studies have shown that if an adversary has complete knowledge on the power grid topology and transmission-line admittance values, he can adjust the false data injection attack vector such that the attack remains undetected and successfully passes the residue-based bad data detection tests that are commonly used in power system state estimation. However, in this paper, we explain that a realistic false data injection attack is essentially an attack with incomplete information due to the attackers lack of real-time knowledge with respect to various grid parameters and attributes such as the position of circuit breaker switches and transformer tap changers and also because of the attackers limited physical access to most grid facilities. We mathematically characterize false data injection attacks with incomplete information from both the attackers and grid operators viewpoints. Furthermore, we introduce a novel vulnerability measure that can compare and rank different power grid topologies against such attacks. To the best of our knowledge, this paper is the first study to investigate false data injection attacks with line admittance uncertainty.

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.

Achieving Optimality and Fairness in Autonomous Demand Response: Benchmarks and Billing Mechanisms

Autonomous demand response (DR) programs are scalable and result in a minimal control overhead on utilities. The idea is to equip each user with an energy consumption scheduling (ECS) device to automatically control the users flexible load to minimize his energy expenditure, based on the updated electricity pricing information. While most prior works on autonomous DR have focused on coordinating the operation of ECS devices in order to achieve various system-wide goals, such as minimizing the total cost of generation or minimizing the peak-to-average ratio in the load demand, they fall short addressing the important issue of fairness. That is, while they usually guarantee optimality, they do not assure that the participating users are rewarded according to their contributions in achieving the overall systems design objectives. Similarly, they do not address the important problem of co-existence when only a sub-set of users participate in a deployed autonomous DR program. In this paper, we seek to tackle these shortcomings and design new autonomous DR systems that can achieve both optimality and fairness. In this regard, we first develop a centralized DR system to serve as a benchmark. Then, we develop a smart electricity billing mechanism that can enforce both optimality and fairness in autonomous DR systems in a decentralized fashion.

Opportunities and challenges for data center demand response

This paper surveys the opportunities and challenges in an emerging area of research that has the potential to significantly ease the incorporation of renewable energy into the grid as weil as electric power peak-load shaving: data center demand response. Data center demand response sits at the intersection of two growing fields: energy efficient data centers and demand response in the smart grid. As such, the literature related to data center demand response is sprinkled across multiple areas and worked on by diverse groups. Our goal in this survey is to demonstrate the potential of the field while also summarizing the progress that has been made and the challenges that remain.

Exploring smart grid and data center interactions for electric power load balancing

The operation of a data center consumes a tremendous amount of electricity, and the energy cost accounts for a large portion of the data centers operation cost. This leads to a growing interest towards reducing the energy cost of data centers. One approach advocated in recent studies is to distribute the computation workload among multiple geographically dispersed data centers by exploiting the electricity price differences. However, the impact of load redistributions on the power grid is not well understood yet. This paper takes the first step towards tackling this important issue, by studying how the power grid can take advantage of the data centers load distribution proactively for the purpose of power load balancing. We model the interactions between power grid and data centers as a two-stage problem, where the power grid operator aims to balance the electric power load in the first stage, and the data centers seek to minimize their total energy cost in the second stage. We show that this two-stage problem is a bilevel program with an indefinite quadratic objective function, which cannot be solved efficiently using standard convex optimization algorithms. Therefore, we reformulate this bilevel optimization problem as a linear program with additional finite complementarity slackness conditions, and propose a branch and bound algorithm to attain the globally optimal solution. The simulation results demonstrate that our proposed scheme can improve the load balancing performance by around 12% in terms of the electric load index and reduce the energy cost of data centers by 46% on average.

Independent distributed generation planning to profit both utility and DG investors

Most current regulations allow small-scale electric generation facilities to participate in distributed generation (DG) with few requirements on power-purchase agreements. However, in this paper, it is shown that distribution companies can alternatively encourage DG investors into DG contracts that can significantly benefit the utility network. In this regard, a new algorithm is proposed to determine the best sites, sizes, and optimal payment incentives under such special contracts for committed-type DG projects to offset distribution network investment costs. On one hand, the aim is to allocate DGs such that the present value profit gained by the distribution company is maximized via procuring power from DGs and the market at a minimum expense. On the other hand, each DG units individual profit is taken into account to assure that private DG investment remains economical. The algorithm is verified in various cases and the impacts of different factors are accordingly studied.

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).

Optimal Demand Bidding for Time-Shiftable Loads

Time-shiftable loads have recently received an increasing attention due to their role in creating load flexibility and enhancing demand response and peak-load shaving programs. In this paper, we seek to answer the following question: how can a time-shiftable load, that itself may comprise of several smaller time-shiftable subloads, submit its demand bids to the day-ahead and real-time markets so as to minimize its energy procurement cost? Answering this question is challenging because of the inter-temporal dependencies in choosing the demand bids for time-shiftable loads and due to the coupling between demand bid selection and time-shiftable load scheduling problems. Nevertheless, we answer the above question for different practical bidding scenarios and based on different statistical characteristics of practical market prices. In all cases, closed-form solutions are obtained for the optimal choices of the price and energy bids. The bidding performance is then evaluated in details by examining several case studies and analyzing actual market price data.