Capturing Aggregate Flexibility in Demand Response

Flexibility in electric power consumption can be leveraged by Demand Response (DR) programs. The goal of this paper is to systematically capture the inherent aggregate flexibility of a population of appliances. We do so by clustering individual loads based on their characteristics and service constraints. We highlight the challenges associated with learning the customer response to economic incentives while applying demand side management to heterogeneous appliances. We also develop a framework to quantify customer privacy in direct load scheduling programs.Comment: Submitted to IEEE CDC 201

Optimizing energy storage participation in emerging power markets

The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the electricity grid. A promising class of potential demand response providers includes energy storage systems (ESSs). This paper evaluates the benefits of using various types of novel ESS technologies for a variety of emerging smart grid demand response programs, such as regulation services reserves (RSRs), contingency reserves, and peak shaving. We model, formulate and solve optimization problems to maximize the net profit of ESSs in providing each demand response. Our solution selects the optimal power and energy capacities of the ESS, determines the optimal reserve value to provide as well as the ESS real-time operational policy for program participation. Our results highlight that applying ultra-capacitors and flywheels in RSR has the potential to be up to 30 times more profitable than using common battery technologies such as LI and LA batteries for peak shaving

Optimizing Energy Storage Participation in Emerging Power Markets

The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the electricity grid. A promising class of potential demand response providers includes energy storage systems (ESSs). This paper evaluates the benefits of using various types of novel ESS technologies for a variety of emerging smart grid demand response programs, such as regulation services reserves (RSRs), contingency reserves, and peak shaving. We model, formulate and solve optimization problems to maximize the net profit of ESSs in providing each demand response. Our solution selects the optimal power and energy capacities of the ESS, determines the optimal reserve value to provide as well as the ESS real-time operational policy for program participation. Our results highlight that applying ultra-capacitors and flywheels in RSR has the potential to be up to 30 times more profitable than using common battery technologies such as LI and LA batteries for peak shaving.Comment: The full (longer and extended) version of the paper accepted in IGSC 201

Which electricity market design to encourage the development of demand response?

International audienceDemand response is a cornerstone problem in electricity markets under climate change constraints. Most liberalized electricity markets have a poor track record at encouraging the deployment of smart meters and the development of demand response. In Europe, different models are considered for demand response, from a development under a regulated regime to a development under competitive perspectives. In this paper focusing on demand response and smart metering for mid-­‐size and small consumers, we investigate which types of market signals should be sent to demand managers to see demand response emerge as a competitive activity. Using data from the French power system over nine years , we compare the possible market design options which would enable the development of demand response. Our simulations demonstrate that under the current market rules demand response is not a profitable activity in the French electricity industry. Introducing a capacity market could bring additional revenues to demand response providers and improve incentives to put in place demand response programs in a market environment

Chilled Water Thermal Storage System and Demand Response at the University of California at Merced

University of California at Merced is a unique campus that has benefited from intensive efforts to maximize energy efficiency, and has participated in a demand response program for the past two years. Campus demand response evaluations are often difficult because of the complexities introduced by central heating and cooling, non-coincident and diverse building loads, and existence of a single electrical meter for the entire campus. At the University of California at Merced, a two million gallon chilled water storage system is charged daily during off-peak price periods and used to flatten the load profile during peak demand periods, further complicating demand response scenarios. The goal of this research is to study demand response savings in the presence of storage systems in a campus setting. First, University of California at Merced is described and its participation in a demand response event during 2008 is detailed. Second, a set of demand response strategies were pre-programmed into the campus control system to enable semi-automated demand response during a 2009 event, which is also evaluated. Finally, demand savings results are applied to the utility’s DR incentives structure to calculate the financial savings under various DR programs and tariffs

Application of distinct demand response program during the ramping and sustained response period

The environmental concerns around energy, namely electricity, have driven attention to innovative approaches to fostering consumers participation in the whole energy system management. Accordingly, the concept of demand response provides incentives and signals no consumers to change the normal consumption patterns to increase the use of renewables, for example. The problem is that such response of consumers has a large amount of uncertainty. This paper proposes a methodology in which different demand response programs are activated and deactivated during an event to cover the demand response deviations from the target. Even after achieving the response target, if the actual response of consumers is reduced to a critical level, additional programs are activated. The proposed approach considers consumers participating in an aggregate way, supported by an aggregator. The case study in this paper accommodates three demand response programs, showing how different consumers are activated and remunerated for the provision of consumption reduction. It has been seen that the proposed methodology is flexible as desired to accommodate the uncertainty of consumers’ responses.This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project COLORS (PTDC/EEI-EEE/28967/2017). The work has been done also in the scope of projects UIDB/00760/2020, and CEECIND/02887/2017, financed by FEDER Funds through COMPETE program and from National Funds through (FCT) . The authors would like to acknowledge the contribution of Omid Abrishambaf to this workinfo:eu-repo/semantics/publishedVersio

A truthful incentive mechanism for emergency demand response in colocation data centers

Data centers are key participants in demand response programs, including emergency demand response (EDR), where the grid coordinates large electricity consumers for demand reduction in emergency situations to prevent major economic losses. While existing literature concentrates on owner-operated data centers, this work studies EDR in multi-tenant colocation data centers where servers are owned and managed by individual tenants. EDR in colocation data centers is significantly more challenging, due to lack of incentives to reduce energy consumption by tenants who control their servers and are typically on fixed power contracts with the colocation operator. Consequently, to achieve demand reduction goals set by the EDR program, the operator has to rely on the highly expensive and/or environmentally-unfriendly on-site energy backup/generation. To reduce cost and environmental impact, an efficient incentive mechanism is therefore in need, motivating tenants’ voluntary energy reduction in case of EDR. This work proposes a novel incentive mechanism, Truth-DR, which leverages a reverse auction to provide monetary remuneration to tenants according to their agreed energy reduction. Truth-DR is computationally efficient, truthful, and achieves 2-approximation in colocation-wide social cost. Trace-driven simulations verify the efficacy of the proposed auction mechanism.published_or_final_versio

Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids

In recent deregulated power systems, demand response (DR) has become one of the most cost-effective and efficient solutions for smoothing the load profile when the system is under stress. By participating in DR programs, customers are able to change their energy consumption habits in response to energy price changes and get incentives in return. In this paper, we study the effect of various time-based rate (TBR) programs on the stochastic day-ahead energy and reserve scheduling in residential islanded microgrids (MGs). An effective approach is presented to schedule both energy and reserve in presence of renewable energy resources (RESs) and electric vehicles (EVs). An economic model of responsive load is also proposed on the basis of elasticity factor to model the behavior of customers participating in various DR programs. A two-stage stochastic programming model is developed accordingly to minimize the expected cost of MG under different TBR programs. To verify the effectiveness and applicability of the proposed approach, a number of simulations are performed under different scenarios using real data; and the impact of TBR-DR actions on energy and reserve scheduling are studied and compared subsequently

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

Various approaches for power balancing in grid-connected and islanded microgrids

One of the promising solutions to reduce power imbalance, an undesired impact of intermittent renewable energy sources, is to supply the loads by means of local distributed energy resources in the form of a microgrid. Microgrids offer several benefits such as reduction of line losses, increased system reliability, and maximum utilisation of local energy resources. A microgrid, during its islanded operation, is more susceptible to the frequency and voltage fluctuation caused by a sudden dispatch either from the generation or load. Therefore, additional control is required to manage either the output power from the generation side or the demand from the end-user side. Thus, appropriate and efficient control and monitoring systems need to be installed. However, the cost of such a system will reduce the rate of investment return on microgrid projects. This research has focused on developing various techniques to maintain the voltage and frequency within acceptable limits in microgrids, taking into account various influencing factors. This study proposes an additional active power management technique through the use of inverters, that can maintain the microgrid’s frequency when the generated power in the microgrid is much higher than its demand. Also, to facilitate the microgrid’s transition from grid-connected to islanded mode, the inverters can be controlled with a soft starting ramp. Moreover, a control function employing a droop control method is proposed in order to reduce the output power of the renewable sources when the microgrid frequency is much higher than the nominal frequency. On the other hand, when the demand is higher than the generated power, managing the demand under a demand response program is proposed as a means of maintaining the microgrid stability. This is an inexpensive solution which will not reduce the rate of investment return on the microgrid project. However, this requires the installation of appropriate enabling technologies at the utility and end-user sides. Moreover, the participation from demand response participants is influenced by the profit earned from engaging in the program. Therefore, in this research, the technical and economic benefits of demand response deployment are analysed in detail. The execution of the demand response program through load-shifting, reducing the appliances’ consumed power, and load-shedding causes customer discomfort. To minimise this discomfort, in this thesis, suitable strategies are suggested for various groups of loads. Furthermore, each load profile contains information on its capacity, flexibility, and operating time. The proposed approach ensures that the loads with a larger capacity and flexibility are the most preferred ones to be controlled during demand response events so that customer discomfort and the number of affected loads can be minimised. Also, this study examines the load’s economic value, power losses, emission factor, and cost of energy production to maximise the microgrid operator’s profit as a result of deploying the demand response program. Meanwhile, to encourage end-users’ engagement in demand response programs, the microgrid operator should offer incentives to the customer as compensation for any incurred costs and discomfort felt. The given incentives should be such that both the microgrid operator and the end-user gain the maximum profit. Therefore, this study proposes an approach for calculating the level of incentives that should be given to the participants by comparing the differences between ongoing revenue and the cost of energy with and without demand response