Conference Paper

Demand Response: From Classification to Optimization Techniques in Smart Grid

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Abstract

In conventional grids, consumer has not been con-sidered for solving the problems associated with electric indus-try. In order to meet the ever increasing consumers' demand, conventional methods primarily rely on increasing generation capacity which is not a feasible solution due to limited resources. Thus, the overall efficiency of electrical networks needs to be improved. From this perspective, the idea of smart grids has transformed the conventional power system into an intelligent and smart one. Smart grid is not a single technology, rather, it is merger of electrical power networks with communications network. Moreover, there are two basic players in the smart grid; utility and consumer. In response to different pricing schemes, introduced by the utility, smart grid transforms the consumer into a prosumer via Demand Response (DR). Thus, enabling the consumer to become an important player in energy management and optimization. This paper embeds a two fold contribution; (i) classification of DR techniques based on the chosen criteria, and (ii) distinctive discussion of latest DR optimization techniques. It is foreseen that this paper will help in determining future research directions and design efforts for developing DR techniques.

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... Various studies distinguished between different categories of DR strategies related to electricity systems. The most usual classification of DR strategies related to the electricity system is based on motivators for the customers: (1) price-based motivator and (2) incentivebased motivator [140][141][142][143]. The incentive-based DR were described differently in the various studies. ...
... The incentive-based DR were described differently in the various studies. Some of the categories of incentive-based DR are direct load control, interruptible service, and emergency DR [1,[140][141][142]. Ahmad et al. [142] also suggested DR classifications based on control information (centralized or distributed). ...
... Some of the categories of incentive-based DR are direct load control, interruptible service, and emergency DR [1,[140][141][142]. Ahmad et al. [142] also suggested DR classifications based on control information (centralized or distributed). ...
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The highly varying character of district heating (DH) demand results in low capacity utilization of the DH plants, as well as increased use of fossil fuels during peak demand. The aim of this study is to present an overview and a comprehensive classification of measures intended to manage these load variations. A systematic literature review was conducted based on previously defined search strings as well as inclusion and exclusion criteria. Two scientific databases were used as data sources. Based on 96 detected publications, the measures were categorized as (1) complementing DH production in heat-only boilers (HOBs), or geothermal or booster heat pumps (HPs) (usually controlled by the DH company), (2) thermal energy (TE) storage in storage units or in the network (controlled by the company), and (3) demand side measures, which can be strategic demand increase, direct demand response (DR), or indirect DR. While the company has control over direct DR (e.g., thermal storage in the thermal mass of the buildings), indirect DR is based on communication between the customer and the company, where the customer has complete control. The multi-disciplinary nature of this topic requires an interdisciplinary approach.
... Resorting to DR loads by the EMS is a suitable method for enhancing the power system reliability, reducing costs, bettering environmental problems, cutting down the market power, and rendering better services to smart grids' consumers. The US energy department defines demand response (DR) as follows: "A tariff or plan which provides incentives for changes in consumers' energy consumption patterns, which occurs in response to variations in electricity price at different time periods or paying up incentives to customers to motivate them in using up less amount of electricity at a time when the electricity market price is high or when the reliability of the main grid is compromised" [30]. ...
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... DSM encompasses the entire range of management functions associated with directing demand-side activities, including program planning, evaluation, implementation and monitoring. [4], [5]. At the same time, DSM technology with key enabling factors, such as smart meters deployment, real-time monitoring, and with its playing field for aggregators, prosumers and other flexibility providers can also contribute to a new role of Distribution System Operators (DSOs). ...
... Further in this strategy loads are curtailed to maintain continuous supply to the customers. Demand side management (DSM) and demand response (DR) programs have different optimization techniques like mixed integer linear programming, evolutionary algorithms and convex optimization [13]. In [14] author proposed a comparison of different optimization techniques for energy resources in smart home environment. ...
... After determining the typical daily load curve for consumption in campus buildings, three techniques for Demand Response (DR) were developed in order to reduce losses and increase energy efficiency of the system: Strategic Conservation, Energy Efficiency and Peak Clipping [17], [18]. To apply each technique, an algorithm was developed in Matlab®; as this tool is linked with PI System®, data are stored on the server. ...
Chapter
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This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.
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This paper presents design considerations for a centralized load controller to control thermostatically controlled appliances (TCAs) for continuous regulation reserves (CRRs). The controller logics for setting up the baseline load, generating priority lists, issuing dispatch commands, and tuning the simplified forecaster model using measurement data are described. To study the impacts of different control parameter settings on control performance and device lifetimes, a system consisting of 1000 heating, ventilating, and air-conditioning (HVAC) units in their heating modes is modeled to provide a CRR 24 hours a day. Four cases are modeled to evaluate the impact of forecasting errors, minimum HVAC turn-off times, response delays, and consumer overrides. The results demonstrate that a centralized TCA load controller can provide robust, good quality CRRs with reduced communication needs for the two-way communication network and inexpensive load control devices. Most importantly, because the controller precisely controls the aggregated HVAC load shapes while maintaining load diversity, the controllable and measurable load services that it provides can be used for many other demand response applications, such as peak shaving, load shifting, and arbitrage.
Conference Paper
We propose a consumption scheduling mechanism for home area load management in smart grid using integer linear programming (ILP) technique. The aim of the proposed scheduling is to minimise the peak hourly load in order to achieve an optimal (balanced) daily load schedule. The proposed mechanism is able to schedule both the optimal power and the optimal operation time for power-shiftable appliances and time-shiftable appliances respectively according to the power consumption patterns of all the individual appliances. Simulation results based on home and neighbourhood area scenarios have been presented to demonstrate the effectiveness of the proposed technique.
Article
Several factors support more deployment of real-time pricing (RTP); including recent developments in the area of smart metering, regulators interest in promoting demand response programs and well-organized electricity markets. This paper first reviews time-based electricity pricing and the main barriers and issues to fully unleash benefits of RTP programs. Then, a day-ahead real-time pricing (DA-RTP) model is proposed, which addresses some of these issues. The proposed model can assist a retail energy provider and/or a distribution company (DISCO) to offer optimal DA hourly prices using smart metering. The real-time prices are determined through an optimization problem which seeks to maximize the electricity provider's profit, while considering consumers' benefit, minimum daily energy consumption, consumer response to posted electricity prices, and distribution network constraints. The numerical results associated with Ontario electricity tariffs indicate that instead of directly posting DA market prices to consumers, it would be better to calculate optimal prices which would yield higher benefit both for the energy provider and consumers.
Conference Paper
Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of domestic technologies have been developed to improve this efficiency. These technologies on their own already improve the efficiency, but more can be gained by a combined management. Multiple optimization objectives can be used to improve the efficiency, from peak shaving and virtual power plant (VPP) to adapting to fluctuating generation of wind turbines. In this paper a generic management methodology is proposed applicable for most domestic technologies, scenarios and optimization objectives. Both local scale optimization objectives (a single house) and global scale optimization objectives (multiple houses) can be used. Simulations of different scenarios show that both local and global objectives can be reached.
Conference Paper
This paper proposes a distributed framework for demand response and user adaptation in smart grid networks. In particular, we borrow the concept of congestion pricing in Internet traffic control and show that pricing information is very useful to regulate user demand and hence balance network load. User preference is modeled as a willingness to pay parameter which can be seen as an indicator of differential quality of service. Both analysis and simulation results are presented to demonstrate the dynamics and convergence behavior of the algorithm.
Conference Paper
This paper is concerned with scheduling of demand response among different residences and a utility company. The utility company has a cost function representing the cost of providing energy to end-users, and this cost can be varying across the scheduling horizon. Each end-user has a “must-run” load, and two types of adjustable loads. The first type must consume a specified total amount of energy over the scheduling horizon, but the consumption can be adjusted across different slots. The second type of load has adjustable power consumption without a total energy requirement, but operation of the load at reduced power results in dissatisfaction of the end-user. The problem amounts to minimizing the total cost electricity plus the total user dissatisfaction (social welfare), subject to the individual load consumption constraints. The problem is convex and can be solved by a distributed subgradient method. The utility company and the end-users exchange Lagrange multipliers-interpreted as pricing signals-and hourly consumption data through the Advanced Metering Infrastructure, in order to converge to the optimal amount of electricity production and the optimal power consumption schedule.
Conference Paper
In this paper, we consider deployment of energy consumption scheduling (ECS) devices in smart meters for autonomous demand side management within a neighborhood, where several buildings share an energy source. The ECS devices are assumed to be built inside smart meters and to be connected to not only the power grid, but also to a local area network which is essential for handling two-way communications in a smart grid infrastructure. They interact automatically by running a distributed algorithm to find the optimal energy consumption schedule for each subscriber, with an aim at reducing the total energy cost as well as the peak-to-average-ratio (PAR) in load demand in the system. Incentives are also provided for the subscribers to actually use the ECS devices via a novel pricing model, derived from a game-theoretic analysis. Simulation results confirm that our proposed distributed algorithm significantly reduces the PAR and the total cost in the system.
Article
This paper analyzes data from 483 households that took part in a critical-peak pricing (CPP) experiment between July and September 2004. Using a regression-based approach to quantify hourly baseline electric loads that would have occurred absent CPP events, we show a statistically significant average participant response in each hour. Average peak response estimates are provided for each of twelve experimental strata, by climate zone and building type. Results show that larger users respond more in both absolute and percentage terms, and customers in the coolest climate zone respond most as a percentage of their baseline load. Finally, an analysis involving the two different levels of critical-peak prices – $0.50/kWh and $0.68/kWh – indicates that households did not respond more to the higher CPP rate.
Conference Paper
In this paper, we present demand-side energy management under real-time demand-response pricing as a task scheduling problem which is NP-hard. Using minmax as the objective, we show that the schedule produced by our minMax scheduling algorithm has a number of salient advantages: significant peak-shaving, cost reduction, and risk-aversion for the consumers. We prove that our algorithm finds near-optimal solutions and our simulation study show that the actual performance is better than the worst-case bound. The algorithm is simple to implement and efficient at the scale of large enterprises.
Article
Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
Article
David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.
Integer Linear Program–Branch and bound method
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