Mechanism Design for Demand Response Programs

Demand Response (DR) programs serve to reduce the consumption of electricity at times when the supply is scarce and expensive. The utility informs the aggregator of an anticipated DR event. The aggregator calls on a subset of its pool of recruited agents to reduce their electricity use. Agents are paid for reducing their energy consumption from contractually established baselines. Baselines are counter-factual consumption estimates of the energy an agent would have consumed if they were not participating in the DR program. Baselines are used to determine payments to agents. This creates an incentive for agents to inflate their baselines. We propose a novel self-reported baseline mechanism (SRBM) where each agent reports its baseline and marginal utility. These reports are strategic and need not be truthful. Based on the reported information, the aggregator selects or calls on agents to meet the load reduction target. Called agents are paid for observed reductions from their self-reported baselines. Agents who are not called face penalties for consumption shortfalls below their baselines. The mechanism is specified by the probability with which agents are called, reward prices for called agents, and penalty prices for agents who are not called. Under SRBM, we show that truthful reporting of baseline consumption and marginal utility is a dominant strategy. Thus, SRBM eliminates the incentive for agents to inflate baselines. SRBM is assured to meet the load reduction target. SRBM is also nearly efficient since it selects agents with the smallest marginal utilities, and each called agent contributes maximally to the load reduction target. Finally, we show that SRBM is almost optimal in the metric of average cost of DR provision faced by the aggregator

Greening Multi-Tenant Data Center Demand Response

Data centers have emerged as promising resources for demand response, particularly for emergency demand response (EDR), which saves the power grid from incurring blackouts during emergency situations. However, currently, data centers typically participate in EDR by turning on backup (diesel) generators, which is both expensive and environmentally unfriendly. In this paper, we focus on "greening" demand response in multi-tenant data centers, i.e., colocation data centers, by designing a pricing mechanism through which the data center operator can efficiently extract load reductions from tenants during emergency periods to fulfill energy reduction requirement for EDR. In particular, we propose a pricing mechanism for both mandatory and voluntary EDR programs, ColoEDR, that is based on parameterized supply function bidding and provides provably near-optimal efficiency guarantees, both when tenants are price-taking and when they are price-anticipating. In addition to analytic results, we extend the literature on supply function mechanism design, and evaluate ColoEDR using trace-based simulation studies. These validate the efficiency analysis and conclude that the pricing mechanism is both beneficial to the environment and to the data center operator (by decreasing the need for backup diesel generation), while also aiding tenants (by providing payments for load reductions).Comment: 34 pages, 6 figure

Reward/Penalty Design in Demand Response for Mitigating Overgeneration Considering the Benefits from Both Manufacturers and Utility Company

The high penetration of renewable sources in electricity grid has led to significant economic, environmental, and societal benefits. However, one major side effect, overgeneration, due to the uncontrollable property of renewable sources has also emerged, which becomes one of the major challenges that impedes the further large-scale adoption of renewable technology. Electricity demand response is an effective tool that can balance the supply and demand of the electricity throughout the grid. In this paper, we focus on the design of reward/penalty mechanism for the demand response programs aiming to mitigate the overgeneration. The benefits for both manufacturers and utility companies are formulated as the function of reward and penalty. The formulation is solved using particle swarm optimization so that the benefit from both supply side can be maximized under the constraint the benefit of customer side is not sacrificed. A numerical case study is used to verify the effectiveness of the proposed method

A Generic Architecture For Demand Response: The ALL4Green Approach

Demand Response is a mechanism used in power grids to manage customers’ power consumption during critical situations (e.g. power shortage). Data centres are good candidates to participate in Demand Response programs due to their high energy use. In this paper, we present a generic architecture to enable Demand Response between Energy Provider and Data Centres realised in All4Green. To this end, we show our three-level concept and then illustrate the building blocks of All4Green’s architectural design. Furthermore, we introduce the novel aspects of GreenSDA and GreenSLA for Energy Provider–Data centre sub-ecosystem as well as Data centre–IT Client sub-ecosystem respectively. In order to further reduce energy consumption and CO2 emission, the notion of data centre federation is introduced: savings can be expected if data centres start to collaborate by exchanging workload. Also, we specify the technological solutions necessary to implement our proposed architectural approach. Finally, we present preliminary proof-of-concept experiments, conducted both on traditional and cloud computing data centres, which show relatively encouraging results

A demand responsive bidding mechanism with price elasticity matrix

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 219-220).In the past several decades, many demand-side participation features have been applied in the electricity power systems. These features, such as distributed generation, on-site storage and demand response, add uncertainties to both the short-term and long-term operation of the modem power systems. On the contrary, many modem power systems are characterized by the deregulated market structure. How to operate these features under deregulated power markets is worth consideration. This thesis presents a new demand responsive bidding mechanism in wholesale electricity pools. The proposed bidding mechanism models demand response with Price Elasticity Matrices (PEM). Under the proposed bidding mechanism, the resultant generation schedules and electricity rates become dependent variable on demand response. This relation gives bidding results that are closer to the actual market equilibrium. By applying this bidding mechanism, more efficient market behaviors are achieved in the short term, and generation and transmission resources are better utilizes in the long term. In addition, compared to the market clearing price and generation dispatch schedule settled by the traditional bidding mechanisms, bidding results obtained under our proposed mechanisms are more effective instructions for the design and implementation of demand-side participation programs. This thesis presents the design of the proposed bidding mechanism in terms of its bidding rules, bidding acceptance rules and settlement rules. The bidding mechanism's mathematical model is formulated as an optimization problem.(cont.) Bidding results are obtained as closed-formed solution of the optimization problem. In addition, this thesis presents an improved market interaction algorithm to implement the bidding mechanism. Multiple benefits of applying the bidding mechanism are shown by numerical example under various system statuses and end-user response types.by Jiankang Wang.S.M

Simulation modeling for energy consumption of residential consumers in response to demand side management.

Energy efficiency in the electricity distribution system continues to gain importance as demand for electricity keeps rising and resources keep diminishing. Achieving higher energy efficiency by implementing control strategies and demand response (DR) programs has always been a topic of interest in the electric utility industry. The advent of smart grids with enhanced data communication capabilities propels DR to be an essential part of the next generation power distribution system. Fundamentally, DR has the ability to charge a customer the true price of electricity at the time of use, and the general perception is that consumers would shift their load to a cheaper off-peak period. Consequently, when designing incentives most DR literature assumes consumers always minimize total electricity cost when facing energy consumption decisions. However, in practice, it has been shown that customers often override financial incentives if they feel strongly about the inconvenience of load-shifting arrangements. In this dissertation, an energy consumption model based on consumers‟ response to both cost and convenience/comfort is proposed in studying the effects of differential pricing mechanisms. We use multi-attribute utility functions and a model predictive control mechanism to simulate consumer behavior of using non-thermostatic loads vi (prototypical home appliances) and thermostatically controlled load (HVAC). The distributed behavior patterns caused by risk nature, thermal preferences, household size, etc. are all incorporated using an object-oriented simulation model to represent a typical residential population. The simulation based optimization platform thus developed is used to study various types of pricing mechanisms including static and dynamic variable pricing. There are many electric utilities that have applied differential pricing structures to influence consumer behavior. However, majority of current DR practices include static variable pricings, since consumer response to dynamic prices is very difficult to predict. We also study a novel pricing method using demand charge on coincident load. Such a pricing model is based on consumers‟ individual contribution to the monthly system peak, which is highly stochastic. We propose to use the conditional Markov chain to calculate the probability that the system will reach a peak, and subsequently simulate consumers‟ behavior in response to that peak. Sensitivity analysis and comparisons of various rate structures are done using simulation. Overall, this dissertation provides a simulation model to study electricity consumers‟ response to DR programs and various rate structures, and thus can be used to guide the design of optimal pricing mechanism in demand side management