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Estimating the Profile of Incentive-Based Demand Response (IBDR) by Integrating Technical Models and Social-Behavioral Factors

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Abstract

Demand response (DR) has been widely recognized as an important approach to balance the power grid and reduce peak load of power systems. In order to better estimate the capability and the expense of peak load reduction through DR, we need to obtain the residential load profile and customers’ attitudes toward DR programs. Based on a large-scale online survey collected among over 1,500 customers from New York and Texas in the U.S., this study investigates the relationships among household appliance activities (e.g., electric water heater and air conditioner), load profiles, and incentive-based DR (IBDR) participation for peak load curtailment through reward payment. The daily load profiles of major home appliances are developed. Additionally, this study estimates the expense of reducing the yearly peak of the local grid load. Finally, the study addresses the importance of investigating the multifaceted factors of affecting IBDR participation and provides useful suggestions to utility companies when implementing DR programs.

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... The DR aggregator provides a comprehensive customer service like an integrated energy service provider, because it is hard for customers to evaluate their DR potentials. The DR aggregator can perform an overall data mining of users' behaviors based on the technical models and the social-behavioral survey results [2], [24]- [26]. It is assumed that the customers have an overall understanding of their electricity consumption and DR capacity based on the performance evaluation service provided by the DR aggregator. ...
... This region is called the insensitive area. When the unit price continues to increase and exceeds a i , the customer is willing to enroll in the DR contract and the allowed dispatch power is approximately proportional to the unit price offered by the aggregator [24]. This region is called the responsive area. ...
... In this paper, the piecewise constraints are dealt with by introducing extra 0-1 integer variables. Equations (21)- (24) can be reformulated as shown in (27)-(36). ...
Article
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Demand response (DR) has received much attention for its ability to balance the changing power supply and demand with flexibility. DR aggregators play an important role in aggregating flexible loads that are too small to participate in electricity markets. In this work, a DR operation framework is presented to enable local management of customers to participate in electricity market. A novel optimization model is proposed for the DR aggregator with multiple objectives. On one hand, it attempts to obtain the optimal design of different DR contracts as well as the portfolio management so that the DR aggregator can maximize its profit. On the other hand, the customers' welfare should be maximized to incentivize users to enroll in DR programs which ensure the effective and flexible load control. The consumer psychology is introduced to model the consumers' behavior during contract signing. Several simulation studies are performed to demonstrate the feasibility of the proposed model. The results illustrate that the proposed model can ensure the profit of the DR aggregator whereas the customers' welfare is considered.
... However, these schemes mostly consider abstract appliances and do not take into account either user behavioral models or the complex dynamics of different appliances on power consumption, thus limiting their applicability and effectiveness in practice. A few works focus on the impact of HVAC during periods of high load [6], [26]. However, the authors of [6] only provide preliminary results to support the use of HVAC, while the auhors of [26] proposes a basic flatrate framework that does not incentivize participation due to monthly commitments. ...
... A few works focus on the impact of HVAC during periods of high load [6], [26]. However, the authors of [6] only provide preliminary results to support the use of HVAC, while the auhors of [26] proposes a basic flatrate framework that does not incentivize participation due to monthly commitments. Contributions and key novelties: To the best of our knowledge, our work is the first to design a comprehensive framework for power conservation that simultaneously addresses user engagement and bidding behaviors, specific dynamics of HVAC related to individual homes, and the system operator's overall objective. ...
... To address these shortcomings, Incentive-Based Power Conservation (IBPC) approaches have been introduced, with the goal of engaging users effectively through monetary incentives. Although IBPC incurs in an additional cost for the utility company, a recent study has shown that increasing 100MW generation unit can be as costly as providing monetary rewards up to a period of 36.2 years [26]. Therefore, utility companies are highly incentivized and have wide margins of profitability in using an IBPC approach. ...
... However, the customer opt-out behaviors are uncertain and unknown to LSEs in practice, which brings significant challenges to the real-time DR control. References [8]- [11] indicate that customer DR behaviors are influenced by individual preference and environmental factors. The individual preference relates to customers' intrinsic socio-demographic characteristics, e.g. ...
... 3) Environmental Factors: Based on the empirical investigations in [8]- [11], we present below several key environmental factors that influence customers' opt-out behaviors. In particular, the first three factors are affected by the AC control scheme, thus their dynamics models are introduced as well. ...
... To simplify the expression, we reformulate the AC control constraints (2), the opt-out status transition (3), (8), (11), and the dynamics of environmental factors (5), (6), (9) or (10), for customer i ∈ [N ], as the following compact form (13): ...
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This paper studies the automated control method for regulating air conditioner (AC)-type loads in incentive-based residential demand response (DR). The critical challenge is that the customer responses to load adjustment are uncertain and unknown in practice. In this paper, we formulate the AC control problem in a DR event as a Markov decision process that integrates the indoor thermal dynamics and customer opt-out status transition. Specifically, machine learning techniques including Gaussian process and logistic regression are employed to learn the unknown thermal dynamics model and customer opt-out behavior model, respectively. We consider two typical DR objectives for AC load control: 1) minimizing the total load demand, 2) closely tracking a regulated power trajectory. Based on the Thompson sampling framework, we propose an online DR control algorithm to learn customer behaviors and make real-time AC control schemes. This algorithm considers the influence of various environmental factors on customer behaviors, and is implemented in a distributed fashion to preserve the privacy of customers. Numerical simulations demonstrate the control optimality and learning efficiency of the proposed algorithm. Index Terms-Direct load control, online learning, uncertain customer behavior, distributed algorithm.
... However, the customer opt-out behaviors are uncertain and unknown to LSEs in practice, which brings significant challenges to the real-time DR control. References [8]- [11] indicate that customer DR behaviors are influenced by individual preference and environmental factors. The individual preference relates to customers' intrinsic socio-demographic characteristics, e.g. ...
... 3) Environmental Factors: Based on the empirical investigations in [8]- [11], we present below several key environmental factors that influence customers' opt-out behaviors. In particular, the first three factors are affected by the AC control scheme, thus their dynamics models are introduced as well. ...
... To simplify the expression, we reformulate the AC control constraints (2), the opt-out status transition (3), (8), (11), and the dynamics of environmental factors (5), (6), (9) or (10), for customer i ∈ [N ], as the following compact form (13): ...
Article
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This paper studies the automated control method for regulating air conditioner (AC) loads in incentive-based residential demand response (DR). The critical challenge is that the customer responses to load adjustment are uncertain and unknown in practice. In this paper, we formulate the AC control problem in a DR event as a multi-period stochastic optimization that integrates the indoor thermal dynamics and customer optout status transition. Specifically, machine learning techniques including Gaussian process and logistic regression are employed to learn the unknown thermal dynamics model and customer opt-out behavior model, respectively. We consider two typical DR objectives for AC load control: 1) minimizing the total demand, 2) closely tracking a regulated power trajectory. Based on the Thompson sampling framework, we propose an online DR control algorithm to learn customer behaviors and make real-time AC control schemes. This algorithm considers the influence of various environmental factors on customer behaviors and is implemented in a distributed fashion to preserve the privacy of customers. Numerical simulations demonstrate the control optimality and learning efficiency of the proposed algorithm.
... IBDR programs were proven successful in the residential sector in [13,14]. In the first study [13], 1575 residential units agree to a maximum of one curtailment event per day lasting for 45 minutes only, where the HVAC load is reduced to a predefined level. ...
... IBDR programs were proven successful in the residential sector in [13,14]. In the first study [13], 1575 residential units agree to a maximum of one curtailment event per day lasting for 45 minutes only, where the HVAC load is reduced to a predefined level. A one-time payment is made to all customers at the beginning of the exercise. ...
... The study finds out that payments of $5 per month or $10 per month to each unit can defer the installation of a new gas power plant for 35 years or 12 years, respectively. However, the study in [13] does not consider making higher payments for longer curtailments per day or more than 1 brief activation per day, either. The study was implemented for the summer season only. ...
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This paper proposes a DR program characterized by a novel compensation scheme. The proposed scheme recognizes the different characteristics of curtailment, such as the total length of curtailments within a window of time, or the number of separate curtailment events (i.e. curtailment startup), and compensates the end-user accordingly. The proposed compensation scheme features a piece-wise reward function comprised of two intervals. DR participants receive a onetime reward upfront when they enroll in the DR program and accept a set of predefined curtailment aspects. Curtailment aspects in excess of the agreed quantities are rewarded at a linear rate. This design is tailored to appeal to residential DR participants, and aims to secure sufficient flexibility at minimum cost. The parameters of the smart contract are optimized such that the system's social welfare is maximized. The optimization problem is modeled as a mixed-integer linear program. Consequently, this paper updates the unit-commitment (UC) formulation with the commitment aspects of DR units. The proposed extension to the UC problem considers the critical aspects of DR participation, such as: the total length of interruptions within a window, the frequency of interruptions within a time-window irrespective of their length, and the net energy deviation from the original load profile. Deployment of the smart DR contract in the unit dispatch problem requires translating DR participants' characteristics to their equivalent aspects in conventional thermal generators, such as minimum up time, minimum down-time, start-up and shutdown costs. The obtained results demonstrate significant improvement in social welfare, notable reduction of curtailed renewable energy and reduction in extreme ramping events of conventional generators.
... Many research works have been conducted on aggregating residential space cooling loads, such as air conditioners (ACs) [9][10][11]. With the rapid growth of electric vehicle (EV) adoption, the global electricity demand from the EV fleet will reach nearly 1000 TWh by 2030 [12]. ...
... Previous works reveal that load users' behaviour has a significant impact on the demand aggregation performance [11,13]. Voluntary principle-based demand aggregation programs are widely accepted because users can control their facilities autonomously and opt-out in the demand aggregation anytime, depending on their personal energy consumption preferences [1,14]. ...
... The load reduction by each user responding to signal a and b are also assumed to be one unit and two units, respectively, in Equation (11). ...
Article
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Abstract In systems with high penetration of renewables, demand side resources have been aggregated to facilitate system operation. However, the natural uncertainty and randomness of users' behaviour may deteriorate the demand aggregation performance, including a large mismatch from the expected aggregation target and unnecessary cost while executing aggregation. Here, the most fast‐growing demand side resource, electric vehicle is targeted, and an algorithm based on a multi‐armed bandit approach is proposed to aggregate those electric vehicle demands. In the proposed multi‐armed bandit model, each electric vehicle user's behaviour is viewed as two arms. Then, a combinatorial upper confidence bound mixed sorting algorithm, which selects the optimal set of users participating in demand aggregation, is developed. The case studies show that the proposed method can reduce the demand aggregation mismatch and eliminate the unnecessary cost. Additionally, it can be observed that the user experience is also improved.
... According to [11], heating, ventilation and air conditioners (HVACs) account for 45% of average summer peak-day loads. Also, building's characteristic of thermal storage provides great demand flexibility by shedding and shifting HVAC load because indoor temperature does not change fast due to thermal inertia [12] [13]. Together with electric vehicles (EVs) that have electricity storages, they are the ideal residential DR candidates to provide demand flexibility which is an attribute of Gridinteractive Efficient Buildings (GEB). ...
... C. HVAC Aggregator 1) Single HVAC: For simplicity, the first order thermal transfer function is utilized to model a building's dynamic indoor temperature [12]. Thus, each HVAC in cooling mode can be modeled with the following equations: ...
... ũi,t is a continuous variable in [0, 1], (12) is to obtain the active power of HVAC aggregator, (14) is synchronicity constraint which limits the number of HVACs to be turned ON at the same period, (15) is ramp up/down constraint to limit the state transfer speed of HVACs, (16) is energy constraint to reduce the probability of all HVACs centering at the temperature boundaries [23] in order to improve the load dispatching performance. The SOCi,t is formulated as ...
Preprint
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Residential loads, especially heating, ventilation, and air conditioners (HVACs) and electric vehicles (EVs) have great potentials to provide demand flexibility which is an attribute of Grid-interactive Efficient Buildings (GEB). Under this new paradigm, first, EV and HVAC aggregator models are developed in this paper to represent the fleet of GEBs, in which the aggregated parameters are obtained based on a new approach of data generation and least-squares parameter estimation (DG-LSPE), which can deal with heterogenous HVACs. Then, a tri-level bidding and dispatching framework is established based on competitive distribution operation with distribution locational marginal price (DLMP). The first two levels form a bilevel model to optimize the aggregators payment and to represent the interdependency between load aggregators and the distribution system operator (DSO) using DLMP, while the third level is to dispatch the optimal load aggregation to all residents by the proposed priority list-based demand dispatching algorithm. Finally, case studies on a modified IEEE 33-Bus system illustrate three main technical reasons for payment reduction due to demand flexibility: load shift, DLMP step changes, and power losses. They can be used as general guidelines for better decision-making for future planning and operation of demand response programs.
... An experimental methodology has been introduced in [9] to identify the flexibility of customers in response to financial incentives. The authors have examined the relationships of home appliance usage, energy consumption, and participation in IBDRPs for peak load reduction in [10]. The impact of a time-of-use (TOU) program on the consumption Maximum/minimum production of generator (MW) events [14,15]. ...
... The proposed model aims to schedule the units at minimum production costs without jeopardising the system security when the system encounters contingencies. The objective function (see (10)) covers seven terms, among which terms 1-4 are linked to first-stage choices, and terms 5-7 are associated with the second stage. The first-stage choices are made before the realisation of scenarios in contingencies. ...
... The load profile is divided into three sections, including low consumption (2-8), off-peak (1,9,(14)(15)(16)(23)(24) and peak (10)(11)(12)(13)(17)(18)(19)(20)(21)(22) hours. The value of the lost load (VoLL) is set to 150, 300 and 450 $/MWh for low-load, off-peak and peak periods, respectively. ...
Article
This paper presents a pricing optimisation framework for energy, reserve, and load scheduling of a power system considering demand response (DR). The proposed scheduling framework is formulated as a reliability-constrained unit commitment program to minimise the power system operation costs by finding optimal electricity prices and optimal incentives while guaranteeing the reliability of the system during contingencies. Moreover, customers’ attitude toward the electricity price and incentive adjustment and the effect of their preferences on load scheduling and operation of the system are investigated in various DR programs. The proposed scheme is implemented on an IEEE test system, and the scheduling process with and without DR implementation is discussed in detail by a numerical study. The proposed method helps both the system operators and customers to reliably schedule generation and consumption units and select the proper DR program according to defined prices and incentives in the case of an emergency.
... 53 In DR programs, aggregators recruit flexible commercial, 54 residential, and industrial customers who are willing to shift 55 their load Advanced metering facilities and bidirectional com-56 munication infrastructure make customers able to engage 57 actively in DR schemes. Besides, according to the U.S. 58 Energy information, 38% of the total electricity consumption 59 is devoted to residential customers, who form the largest sec-60 tor [2]. Hence, the decision-making of costumers and their 61 behavior is critical. ...
... That is obtained by Eq.(2).h(R tn ) = ( m γ R tnm R tm m γ R tnm ) denotes 213 the social influence of dissatisfaction , rr denotes weighting 214 factor of social contagion, t denotes the time, n denotes the 215 load, κ t denotes the time coefficient such that κ t ≤ 1 n−1 , and 216R tn denotes the amount of the effect of dissatisfaction diffu-217 sion on the active consumers and prosumers, which in turn is 218 a function of cooperation, peak time rebates of the price of 219 electricity, and sustainability. ...
Article
According to the Department of Energy, demand response provides an opportunity for end-users to play a significant role in the efficiency, reliability, resilience, and sustainability of a power grid. This is made possible owing to the existence of storage devices and diversity of energy sources at the customer level and the advent of the Internet of Things. Social influences and psychological traits of consumers affect their behavior and decision-making. Consequently, there is a necessity to bring the influences of humans, organizations, and societies on the power system together through computational social science into a cyber-physical-social system. Hence, in this paper, we introduce our development of an artificial society of the social demand response of a power system, a well-known approach in computational sociology based on a bottom-up approach, starting from theory. We assume that consumers can engage in demand response to fulfill two aims: save their cost or enhance the sustainability of a power system. The literature concerning sustainability-based demand response is limited to only considering CO2, NOX, and SO2. In addition to NOX, and SO2, we examine the impact of power systems on water pollution, disability-adjusted loss of life year, and exergy in demand response, and provide an environomic-based social demand response. We show that when the level of satisfaction and cooperation of end-user is low, the marginal level of load shaving and improvement in sustainability cannot be fulfilled.
... Fortunately, with the rise of Internet of Things (IoTs) on the demand side, fine-grained environmental and operational data can be obtained and used to support the exploitation and utilization of these adjustable demand-side load resources. Even so, the subjective willingness of users can hardly be quantified via these operational data, and traditional social survey can still play a role to some extent (Labeeuw et al., 2015;Shi et al., 2020). Thus, it is necessary to explore the value of demand-side heterogeneous data, comprised of operational load and survey data, to portray particular characteristics of ALRs such that it can provide power enterprises with a practical tool to design rational DR mechanism and make proper DR strategies to ensure the safety and economy of power system operation. ...
... Although these non-technical factors can hardly be extracted from load profile data, questionnaire survey on powerconsumption behavior can reflect users' choices on these aspects directly, such that the assessment of ALRs will be more realistic based on the load and survey data fusion. The authors of Ref. (Shi et al., 2020). conducted a large-scale online survey on users' behavior to participate incentive-based DR (IBDR) programs in New York and Texas in the U.S., based on which the capability and expense of two main reductive household load, i.e. the load of EWH and HVAC, was estimated with related load modeling. ...
Article
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Adjustable resources on the demand side of power system plays a vital role to improve operational flexibility of future low-carbon power system integrated with high-penetration renewable generations. While, these demand-side resources may underperform their expected potentials, due to the lack of understanding on consumers’ refined behaviors. Facing the flexibility improvement of future power system, refined portrait structure of single user combining load characteristics and subjective behavior, is constructed with multi-dimension label system from 4 aspects, including energy consumption and load characteristics, adjustable potential, behavioral awareness and user’s nature. Aiming at supplying demand response service, several key indexes are selected and further evaluated here, via data-driven load character analysis and social-survey-driven user’s subjective consciousness mining based on comprehensive evaluation with combination weighting approach. For practical application to demand response decision making, large-scale user adjustable resource is evaluated and classified based on multivariate density-based clustering algorithm. Numerical results show the feasibility and rationality of the proposed assessment method.
... DR has received great attention from energy policymakers. Implementing DR programs is one effective approach to decreasing or shifting energy demand by reducing customers' electricity usage during peak hours in response to changes in the electricity price [101], [164], [165]. One of the major benefits of DR is to help defer or avoid investment in new power generation or transmission capacity; other benefits of DR include securing power Table 2 Multidimensional Challenges of Implementing DR and Smart Grid Technologies supply, improving system restoration capacity, avoiding power outages, reducing costly network reinforcements, improving the use of renewable sources, providing power frequency regulation services, reducing greenhouse gas emissions, and so on [166]. ...
... These social-demographical and household characteristics can influence residents' energy habits and household activities, which also influences residents' acceptance of the DR program. For example, a largescale survey in the United States suggests that household appliance activities (e.g., electric water heaters and ACs) and load profiles are related to incentive-based DR participation for peak load curtailment through reward payment [164]. Another study conducted in Japan suggests that household heterogeneity and multifaceted factors of household activities, scheduling, and behavioral intention to accept DR are related to DR flexibility potential [190]. ...
Article
An increasing number of distributed energy resources (DERs), such as rooftop photovoltaic (PV), electric vehicles (EVs), distributed energy storage, etc., are being integrated into the distribution systems. The rise of DERs has come hand-in-hand with large amounts of data generated and explosive growth in data collection, communication, and control devices. Additionally, a massive number of consumers are involved in the interaction with the power grid to provide flexibility. Electricity consumers, power networks, and communication networks are three main parts of the distribution systems, which are deeply coupled. In this sense, smart distribution systems can be essentially viewed as cyber-physical-social systems. So far, extensive works have been conducted on the intersection of cyber, physical, and social aspects in distribution systems. These works involve two or three of the cyber, physical, and social aspects. Having a better understanding of how the three aspects are coupled can help to better model, monitor, control, and operate future smart distribution systems. In this regard, this paper provides a comprehensive review of the coupling relationships among the cyber, physical, and social aspects of distribution systems. Remarkably, several emerging topics that challenge future cyber-physical-social distribution systems, including applications of 5G communication, the impact of COVID-19, and data privacy issues, are discussed. This paper also envisions several future research directions or challenges regarding cyber-physical-social distribution systems.
... For the D2D resource sharing, Yi et al. studied an incentive mechanism for downlink cellular traffic offloading with social-aware D2D content sharing [30]. In terms of specific applications [31], [32], an incentivebased demand response (IBDR) scheme by social-behavioral factors was proposed for smart grid [31]. Besides, Su et al. have designed an auction game based incentive scheme for cyber-physical-social systems (CPSS) considering the social behaviors of CPSS users [32]. ...
... For the D2D resource sharing, Yi et al. studied an incentive mechanism for downlink cellular traffic offloading with social-aware D2D content sharing [30]. In terms of specific applications [31], [32], an incentivebased demand response (IBDR) scheme by social-behavioral factors was proposed for smart grid [31]. Besides, Su et al. have designed an auction game based incentive scheme for cyber-physical-social systems (CPSS) considering the social behaviors of CPSS users [32]. ...
Article
Recently, wireless edge networks have realized intelligent operation and management with edge artificial intelligence (AI) techniques (i.e., federated edge learning). However, the trustworthiness and effective incentive mechanisms of federated edge learning (FEL) have not been fully studied. Thus, the current FEL framework will still suffer untrustworthy or low-quality learning parameters from malicious or inactive learners, which undermines the viability and stability of FEL. To address these challenges, the potential social attributes among edge devices and their users can be exploited, while not included in previous works. In this paper, we propose a novel Social Federated Edge Learning framework (SFEL) over wireless networks, which recruits trustworthy social friends as learning partners. First, we build a social graph model to find like-minded friends, comprehensively considering the mutual trust and learning task similarity. Besides, we propose a social effect based incentive mechanism for better personal federated learning behaviors with both complete and incomplete information. Finally, we conduct extensive simulations with the Erdos-Renyi random network, the Facebook network, and the classic MNIST/CIFAR-10 datasets. Simulation results demonstrate our framework could realize trustworthy and efficient federated learning over wireless edge networks, and it is superior to the existing FEL incentive mechanisms that ignore social effects.
... Ref. [35] discussed the residential psychological impact on incentive design by data-driven approaches. Ref. [36] investigated many social-behavioral factors in IBDR such as habits of home appliance usage, the willingness of DR participation and environmental concern by utilizing survey results from over 1500 customers from New York and Texas in the U.S. From the literature, it is clear that BE provides an instructive understanding of individual decision-making in energy consumption. However, the study incorporating CPFs into techno-economic models to interpret customer response behavior in IBDR incentive strategy design remains low. ...
... To the end, once the unique and optimal strategy of GO (p * GO Þ is obtained, subsequently, the best strategies (p * LA;k ) for LA k2 K and the best responses (E * EO;l ) for EO l2L can be ascertained according to (33) and (36), respectively. In accordance, all customers' best response strategies (D * ¼ fD * 1;k ; D * 2;k ; ……; D * I;k g) can also be traced based on (39). ...
Article
Demand-side resources play a significant role in enhancing energy effiency and decarbonization. Performing demand curtailment will psychologically disturb end-customers' comfort and affect decision-making. The penetration of battery energy storage systems(BESSs) in electricity grids introduces another response resource to the grid operator (GO). Therefore, it’s important to investigate the effect of different customer psychological factors(CPFs) on incentive-based demand response(IBDR) strategy in the system with diversified response resources including BESSs. Behavioral economics (BE) interprets individual behavior from psychology and provides insights to behavior modeling. Therefore, this paper applied BE to incorporate CPFs, such as the endowment effect and time-discounting effect. Furthermore, to bring the value of CPFs to the system level, an IBDR model considering CPFs and BESSs (CE-IBDR) is proposed by following the Stackelberg game theory. Upon the participation of load aggregators(LAs) and BESSs operator(EO) in IBDR, the model extends two-party hierarchical levels to four-party spinning from the GO, EOs, LAs and end-customers by extending the two-party Stackelberg game to a two-loop Stackelberg game. Results show that without incorporating CPFs into the model will result deviation in interpreting customer behavior. BESSs is preferred reponse resource than load reduction due to the pressure to incentive end-customers with high endowment valuation.
... The heat load for the domestic hot water is modeled using the first-order dynamic model [39,40], as follows: ...
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The multi-energy system (MES) provides a good environment for the local consumption of renewable energy such as wind and solar power because of its high operational flexibility. In the MES, the hybrid energy storage system (HESS) composed of the battery and thermal storage tank plays an important role in enhancing reliability, economics, and operational flexibility. Hence, determining the optimal size of HESS in the MES is a critical problem but has not received enough attention. In light of this problem, this paper focuses on the optimal HESS planning problem in the community MES (CMES) under diverse uncertainties. Firstly, a two-stage stochastic planning model is proposed for the CMES to coordinate the optimal long-term HESS allocation and the short-term system operation. The thermal inertia in the heating network, space heating demand, and domestic hot water demand is utilized to reduce both the planning and operational cost. Secondly, a deterministic equivalence is proposed for the two-stage planning model to convert it into a mixed-integer linear programming model, which is then solved by off-the-shelf solvers. Finally, simulation results verify the effectiveness of the proposed method. The results reveal that the HESS can enhance the operational flexibility of the CMES but only needs a very few investment costs and prove that the thermal inertia in the CMES can reduce the investment cost of HESS, the fuel, and the operational maintenance cost.
... T HE future distribution network, especially in the urban area, will develop an active system, which is able to dynamically select economic and reliable operation mode according to the consecutive changes caused by external environment such as load fluctuations, intermittent distributed generation (DG) and demand response (DR) [1], [2]. Active distribution network (ADN) [3] is a new operation mode for distribution networks for integrating high penetration DGs. ...
Article
The region-based method has been applied in transmission systems and traditional passive distribution systems without power sources. This paper proposes the model of total quadrant security region (TQSR) for active distribution networks (ADN) with high penetration of distributed generation (DG). Firstly, TQSR is defined as a closed set of all the N-1 secure operation points in the state space of ADN. Then, the TQSR is modeled considering the constraints of state space, normal operation and N-1 security criterion. Then, the characteristics of TQSR are observed and analyzed on the test systems with different DG penetrations. TQSR can be located in any quadrant of the state space. For different DG penetrations, the shape and security features of TQSR are also different. Finally, the region map is discovered, which summarizes the features of different types of distribution networks.
... Usually, during peak demands supplier installing high marginal cost plants to meet the additional demand and may incur a high cost both on supplier and customer. Therefore, economically balancing approach between customer and supplier, during peak hours, has recently more motivated greater utilization of DR programs (Shi and Qingxin 2019). At peak demand, a substantial number of high marginal cost generators are reliably upheld to serve the requisite demand in short-term period (Kirschen 2003b). ...
... Among these mechanisms, offer based mechanisms got the significant amount of attention because they directly incentive users and users can directly see their participation [29]. In offer based DR models, motivation is developed among smart homes to use minimal amount of energy in the given time slot so that grid utility can balance the load curve and can predict the load in the most proficient manner [30]. In this article, we use a subcategory of offer based DR mechanism in which we provide incentives to participating users on the basis of the factor that whether they are contributing in causing peak factor or not. ...
Preprint
In order to efficiently provide demand side management (DSM) in smart grid, carrying out pricing on the basis of real-time energy usage is considered to be the most vital tool because it is directly linked with the finances associated with smart meters. Hence, every smart meter user wants to pay minimum possible amount along with getting maximum benefits. In here, usage based dynamic pricing strategies of DSM plays their role and provide users with specific incentives that help shaping their load curve according to the forecasted load. However, these reported real-time values can leak privacy of smart meter users, which can lead to serious consequences such as spying, etc. Moreover, most of dynamic pricing algorithms charges all users equally irrespective of their contribution in causing peak factor. Therefore, in this paper, we propose a modified usage based dynamic pricing mechanism that only charges the users responsible for causing peak factor. We further integrate the concept of differential privacy to protect the privacy of real-time smart metering data and to calculate accurate billing, we propose a noise adjustment method. Finally, we propose Demand Response enhancing Differential Pricing (DRDP) strategy that effectively enhances demand response along with providing dynamic pricing to smart meter users. We also carry out extensive theoretical analysis for differential privacy guarantees and for cooperative state probability to analyse behaviour of cooperative smart meters. The performance evaluation of DRDP strategy at various privacy parameters show that the proposed strategy outperforms previous mechanisms in terms of dynamic pricing and privacy preservation.
... Constraint (15) indicates that the amount of load shedding cannot exceed the existing load. Generally, thermostatic loads (e.g., air conditioners) are in high priority to be turned off [25]. Based on the assumption that the power factors of both CL and IL are constant at all times, the ratio between the reactive and real powers of a load is given by (16) [26]. ...
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In this paper, a fuel-based distributed generator (DG) allocation strategy is proposed to enhance the distribution system resilience against extreme weather. The long-term planning problem is formulated as a two-stage stochastic mixed-integer programming (SMIP). The first stage is to make decisions of DG siting and sizing under the given budget constraint. In the second stage, a post-extreme-event-restoration (PEER) is employed to minimize the operating cost in an uncertain fault scenario. In particular, this study proposes a method to select the most representative scenarios for the SMIP. First, a Monte Carlo Simulation (MCS) is introduced to generate sufficient scenarios considering random fault locations and load profiles. Then, the number of scenarios is reduced by the K-means clustering algorithm. The advantage of scenario reduction is to make a trade-off between accuracy and computational efficiency. Finally, the SMIP is solved by the progressive hedging algorithm. The case studies of the IEEE 33-bus and 123-bus test systems demonstrate the effectiveness of the proposed algorithm in reducing the expected energy not served (EENS), which is a critical criterion of resilience.
... s(t) is governed by a switching law [23]: ...
Article
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As the penetration of renewable energy continues to increase, stochastic and intermittent generation resources gradually replace the conventional generators, bringing significant challenges in stabilizing power system frequency. Thus, aggregating demand-side resources for frequency regulation attracts attentions from both academia and industry. However, in practice, conventional aggregation approaches suffer from random and uncertain behaviors of the users such as opting out control signals. The risk-averse multi-armed bandit learning approach is adopted to learn the behaviors of the users and a novel aggregation strategy is developed for residential heating, ventilation, and air conditioning (HVAC) to provide reliable secondary frequency regulation. Compared with the conventional approach, the simulation results show that the risk-averse multi-armed bandit learning approach performs better in secondary frequency regulation with fewer users being selected and opting out of the control. Besides, the proposed approach is more robust to random and changing behaviors of the users.
... U.S. utilities spent about $7.7 billion on energy efficiency programs in 2016, although their efforts varied across states [6,7]. Utilities' efforts, overall, included the use of financial incentives to encourage end-users of their products to insulate structures, upgrade furnaces to energy-efficient ones, acquire energy-efficient appliances, and participate in energy demand response programs [8][9][10][11][12]. ...
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This study examines the relationship between energy efficiency policies and state-level energy consumption in the United States. In doing this, we assess tenets of ecological modernization theory, which suggests developed societies can and will leverage technology, including efficiency innovations and policies, to limit human demands and impacts on nature without needing to curb economic growth. We use panel data spanning 2009 through 2016 for all 50 U.S. states. The results from fixed effects regression analysis contradict the ecological modernization proposition regarding the utility of energy efficiency improvement. We show, overall, that state governments’ energy efficiency policies are either positively related to energy consumption or their effects fail to reach statistical significance. Our findings suggest that savings from efficiency improvement are often channeled to expanding production and consumption, which has significant implications for energy use. Thus, while efficiency improvement, in principle, can reduce energy demand and contribute to sustainability, the challenge is how to deploy it without unintentionally incentivizing more consumption.
... Ref. [43] distinguished different energy consumption levels in Xuzhou, Jiangsu Province, China considering energy-saving psychological motivation. Ref. [44] investigated the relationships among household appliance activities and estimated the profile of IBDR considering social-behavioral factors. However, the number of research on approaches for incorporating BE ideas into IBDR scheme design is relatively small. ...
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Incentive-based demand response (IBDR) programs have played an essential role in energy efficiency delivery, especially peak shedding. Recently, utilities have been challenged to consider the implications of low-carbon transitions and the wider benefits of IBDR. What's more, to fully activate demand-side resources, the IBDR scheme's design requires broadening the analysis beyond the traditional disciplines of economic entities and incorporating new psychological cues of customers. In this regard, this paper studies system operator (SO)'s carbon emissions abatement and incentive strategies in peaking shedding events when facing pressure from both emissions tax and customer non-economic response. We develop a trilayer economic-environmental-behavioral IBDR model for incentive price setting and investigate how carbon tax and customer psychological factors (CPFs) affect the scheme design. Initially, the interaction among hierarchical market participators is captured by a trilayer Stackelberg game. Then the SO's problem is formulated as multi-objective to minimize the procurement cost and emission. Moreover, CPFs are incorporated into the model by parameterized assumptions following behavioral economics. Results show that without consideration of CPFs will result in deviation in the DR model. With reasonable carbon prices, IBDR can be an effective tool for both energy efficiency improvement and decarbonization.
... requesting operational control via signals over customers' specific household appliances (such J o u r n a l P r e -p r o o f as electric hot water systems, heat pumps or air conditioners especially in the U.S.) for a limited time period [22]- [24]. ...
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Demand-side response (DSR), via price signals (e.g. tariffs) or via direct load control (DLC), is recognised as vital to the network operations with high levels of renewables. This study aims to measure the interest in electricity tariffs and acceptance of DLC, conducting a survey of 622 households and to identify the socio-demographic and dwelling characteristics associated with the decisions. Firstly, cluster analysis identified four DSR preferences: conservative (19%), reserved (20%), agreeable (34%) and flexible (27%). A multinomial logistic regression test showed that dwelling type, tenure, employment, and education influence DSR preferences. Secondly, ANOVA test showed that the interest in tariffs was significantly different across socio-demographics such as age, gender, and education level. Thirdly, we found that the acceptance of DLC is higher for devices (e.g. heat pumps, electric boilers, PV systems, home batteries) than appliances (e.g. tumble dryers, washing machines, dishwashers, EVs). Chi-square tests showed that employment status, presence of children, gender, age are significant factors for the acceptance of appliance DLC whereas it was dwelling type and education level for devices. These findings highlight the heterogeneity of DSR preferences, thereby pointing to challenges such as perceived control, and socio-technical dynamics are key to achieve high participation in such programmes.
... The proposed schedules are used in the California Building Energy Code Compliance for Residential buildings (CBECC-Res) [7]. In another study, the aggregated EWH load was calculated by analyzing the hot water usage schedules [8]. A typical aggregated load for EWHs has a morning and evening peak, as shown in the study involving 50 water heaters [9]. ...
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Advanced control techniques may be used to establish a virtual power plant to regulate the operation of electric water heaters, which may be regarded as a “uni-directional battery” and a major component of a hybrid residential energy storage system. In order to estimate the potential of regulating water heaters at the aggregated level, factors including user behavior, number of water heaters, and types of water heaters must be considered. This study develops generic water heater load curves based on the data retrieved from large experimental projects for resistive electric water heaters (EWHs) and heat pump water heaters (HPWHs). A community-level digital twin with scalability has been developed to capture the aggregated hot water flow and average hot temperature in the tank. The results in this paper also include the “energy take” in line with the CTA-2045 standard and Energy Star specification. The data from the experiments demonstrated that changing from an EWH to an HPWH reduces electricity usage by approximately 70%. The case study showed that daily electricity usage could be shifted by approximately 14% and 17% by EWH and HPWHs, respectively, compared to their corresponding average power. Another case study showed that both EHWs and HPWHs, coordinated with PV to reduce morning and evening peaks, could shift approximately 22% of the daily electricity.
... Timeshiftable loads include charging of EVs, washing machines, dryers, etc. Among all residential loads, thermostatically controlled loads (TCLs) (e.g., heating, ventilation, and air conditioning (HVAC) systems and electric water heater) and EVs account for a large share of the electricity consumption and have similar energy storage characteristics [4] [135]. These characteristics render them the ideal residential DR candidates that can respond to varying price signals. ...
Preprint
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Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market, but incorporates characteristics of the distribution system, such as high R/X ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This paper first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.
... There are two major types of DR, price-based and incentive-based demand response [5] [6]. The household end-user schedules appliances in ways that minimize the peak consumption and cost by shifting time intervals [7]. In more cases, customer tends to shift the consumption pattern from peak to off-peak to avoid the peak energy expenses; however, this may lead to a new peak on off-peak hours [8][9] [10]. ...
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Balancing electricity consumption and generation in the residential market is essential for power grids. The imbalance of power scheduling between energy supply and demand would definitely increase costs to both the energy provider and customer. This paper proposes a control function to normalize the peak cost and customer discomfort. In this work, we modify an optimization power scheduling scheme by using the inclined-block rate (IBR) and real-time price (RTP) technique to achieve a desired trade-off between electricity payment and consumer discomfort level. For discomfort, an average time delay between peak and off-peak is proposed to minimize waiting time. The simulation results present our model more practical and realistic with respect to the consumption constrained at peak hours.
... Compared with other AC components that have high inertia [134], [135] or a long response time [136], [137], a VSC can be controlled and regulated on a very short time-scale [138]. Generally, in case of a disturbance, internal current control can be finished within 0.05 seconds and the terminal voltage control can proceed within 0.5 seconds. ...
Thesis
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Power systems with voltage source converter-based multi-terminal DC (VSC-MTDC) have received great interest in both the academic and industrial worlds in recent years. The introduction of VSC-MTDC systems into the power system industry brings not only significant benefits but also severe challenges due to the complex structures, different operating behaviors, and dynamic features of VSC-MTDC. State estimation (SE), an important function in the Energy Management System (EMS) for real-time monitoring, has become a challenging issue for VSC-MTDC systems. The traditional approach to dealing with this problem only considers the quasi-steady status of a VSC and ignores its dynamic features. Therefore, an in-depth study is presented in this dissertation to propose a generalized, high-efficiency, and accurate SE method for VSC-MTDC systems. First, a systematic and detailed comparison is presented between two conventional methods, the unified method (UM) and the sequential method (SM) in three aspects, including estimation accuracy, computing speed and robustness. Detailed simulations and in-depth analysis point out the most applicable situations of each method. Results indicate that the SM associated with fast decoupled state estimation (FDSE) has the best overall performance. Therefore, the SM-FDSE is selected as the main method to be improved for SE analysis. Second, graph-based computation is leveraged to facilitate the computing speed of the SM-FDSE. By modeling the system as a graph with vertices and edges, advanced parallel techniques including node-based parallel computing (NPC) and hierarchical parallel computing (HPC) can be used to accelerate SE computation. In addition, in view of the topology of VSC-MTDC systems and the shortcomings of the conventional SM, improved methods are also proposed to gain better convergence speed without compromising accuracy. Third, this dissertation presents an estimation of VSCs with various types of droop control. A new bound-constrained nonlinear least square (BCNLS) algorithm is utilized to estimate VSCs with two-stage droops. In addition, to avoid divergence in the iterative calculation, the Levenberg-Marquardt method is leveraged to adjust searching steps. Finally, another dynamic feature of operating limits on VSCs is taken into consideration for SE analysis. The capability of fast adjustment and self-regulation of VSCs is modeled as the equality constraints on the SM and subsequently solved by using Lagrangian relaxation. Therefore, the operating limits can be correctly reflected in estimations. In addition, the proposed estimator is further enhanced with Hachtel’s Augmented Matrix method to take bad data identification into consideration. Lagrangian multipliers can subsequently be used to help eliminate gross errors。
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Price-based Demand Response (PBDR) programs have been envisaged to motivate customers for changing their consumption patterns by reducing peak demand against financial gains. DR is an emerging strategy to deal with peak demand, energy management, line congestion, reliability, security and economy of contemporary distribution systems. Therefore, it becomes essential to assess overall impact of DR prior to its implementation. However, the job is quite tedious as demand of the customers also depends upon socio-economic factors, human psychology, ambient conditions, etc., besides the dynamic price signal. This paper proposes a comprehensive, more realistic, simple and adaptive mean price-based DR modelling to assess overall impact of PBDR while addressing stochastic demand, and price elasticity of demand (PED) for individual customer. The model suggests dynamic PED to capture the stochastic DR of customers against real-time price signal using historical data. In addition, several DR indices are suggested to assess the impact of DR on several stakeholders. The study on a standard test bench reveals that proposed methodology is simple, robust, quick, and can deliver tangible information in the interest of concerned stakeholders.
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The residential air conditioners (RACs) are increasing rapidly in urban power systems and have been widely considered as good regulation resources for improving the system flexibility and resiliency. However, in practical power systems, it is difficult to comprehensively acquire millions of RACs’ operating data and buildings’ thermal data, which makes the available regulation capacity of RACs tricky to evaluate. To address this issue, this paper proposes a Gaussian Mixture Model (GMM)-based evaluation method by utilizing partial easily observable data. First, a control framework of large-scale RACs is developed to provide regulation services for the power system. Based on the thermal–electrical models of RACs and buildings, a quantification method of the available regulation capacities is proposed under the premise of guaranteeing all the users’ comfortable indoor temperatures. Considering the practical condition of insufficient data acquisition of large-scale heterogeneous RACs, a GMM-based evaluation method is designed to calculate the probability of semi-info RACs’ expected regulation capacities by sampling a small portion of full-info RACs’ characteristics. Moreover, the Expectation Maximization Algorithm and the Bayesian Information Criterion are employed to optimize the multi-dimensional parameters and the component number of the GMM, which significantly improve the evaluation accuracy with lower complexity. The proposed models and methods are verified in a demonstration project on demand response in China.
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Distributed energy resources (DERs) can provide flexibility and promote controllability for distribution networks. However, there are still some obstacles in the management of the small-capacity DERs. In this paper, an aggregated operation model of various heterogeneous DERs in peer-to-peer (P2P) energy trading is proposed. First, a supervised-learning assisted aggregation model of DERs is presented, which aims at estimating the aggregated parameters of heterogeneous DERs. Second, a P2P trading model is proposed for the aggregated DERs. The aggregators can trade with each other in a distributed manner so that privacy can be protected. Third, the fast alternating direction method of multipliers (F-ADMM) is utilized to accelerate the iteration process between the aggregators. Therefore, a quick decision can be made, and less information exchange is required between aggregators, which can reduce the communication burden. The effectiveness of the proposed methodology is verified in case studies.
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This paper presents a systematic review of the research literature that applies quantitative techniques to inform incentive programs and policies promoting pro-environmental behavior and technology adoption among individual consumers. The paper points out that, while the number of active incentive programs is large, there is a dire need for scientific advances that could increase their impact in calculated ways. The expertise of the operations research and management science community, as well as industrial, systems, civil, and environmental engineering experience, appears to be particularly well suited to support such effort. The review covers the research work performed in three areas of practical importance: efficient energy consumption, waste management, and stormwater management. The types of analytical models and data analysis techniques developed to support policy-making in each area are summarized, highlighting the imbalance between the descriptive versus prescriptive contributions made to date.
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As the penetration of renewable energy resources increases, their intrinsic uncertainty and randomness raise significant concerns about the grid’s reliability. With the development of information technologies, residential air-conditioners serve increasing services, including secondary frequency regulation, peak load shaving, and capacity reserve. However, these resources can hardly be allocated economically and reliably. On the one hand, it is unrealistic to provide advanced and expensive control modes for all AC units due to the limited investment cost. On the other hand, the uncertainty of residents’ behavior varying with control modes affects the reliability. Few previous works effectively make a trade-off between economics and reliability, which restricts the value of residential air-conditioners. Thus, this paper proposed an economical and reliable allocation method based on a real survey dataset and the online learning multi-armed bandits framework. This method explores the inherent relationship between the allocation plan’s economics and reliability. We also propose an allocation model which enables system operators to design an economic and reliable plan for each residential area considering ancillary services requirements. A case study in a modified IEEE 9 nodes system illustrates the effectiveness of the proposed method.
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The parametric uncertainties inherent in the models of renewable and sustainable energy systems (RSESs) make the associated decision-making processes of integrated resource operation, planning, and designing profoundly complex. Accordingly, intelligent energy management strategies are recognised as an effective intervention to efficiently accommodate the variability inherent in various input data and integrate distributed demand-side flexibility resources. To identify the key methodological and content gaps in the area of stochastic dispatch and planning optimisation of RSESs in the presence of responsive loads, this paper systematically reviews and synthetically analyses 252 relevant peer-reviewed academic articles. The review reveals that academic studies have utilised a wide variety of methods for the joint quantification of uncertainties and procurement of demand response services, while optimally designing and scheduling RSESs. However, to minimise simulation-to-reality gaps, research is needed into more integrated energy optimisation models that simultaneously characterise a broader spectrum of problem-inherent uncertainties and make behaviourally-founded use of flexible demand-side resources. More specifically, the review finds that while the research in this area is rich in thematic scope, it is commonly associated with strong simplifying assumptions that disconnect the corresponding approaches from reality, and thereby obscure the real challenges of transferring simulations into the real world. Accordingly, based on the descriptive analyses conducted and knowledge gaps identified, the paper provides useful insights into myriad possibilities for new research to more effectively utilise the potential of responsive loads, whilst simultaneously characterising the most salient problem-inherent parametric sources of uncertainty, during the investment planning and operational phases of RSESs.
Chapter
The present evolution of the energy systems is characterized by an increase of small-size solutions, with which new aggregations of local integrated energy systems are emerging. The current policies on cleaner and efficient energy systems are providing opportunities to the development of systems supplied by renewable energy sources, and to the combined production from multiple energy carriers. Regulatory evolutions are enabling the existence and operation of local energy markets and energy communities. This chapter provides an overview of the relevant literature concerning the use of distributed energy resources in the context of local energy systems. The chapter is organized in three parts. In the first part, the aspects addressed include the deployment of local solutions to generate, store, and manage energy at small scale and micro-scale. The second part deals with grid-related aspects and solutions for optimal grid operation. The third part discusses various emergent aspects referring to local energy systems, markets and energy communities, which are gaining relevance in the present and future context of energy transition.
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Residential loads, especially heating, ventilation and air conditioners (HVACs) and electric vehicles (EVs), have great potentials to provide demand flexibility which is an attribute of grid-interactive efficient buildings (GEB). Under this new paradigm, first, EV and HVAC aggregator models are developed in this paper to represent the fleet of GEBs, in which the aggregated parameters are obtained based on a new approach of data generation and least squares parameter estimation (DG-LSPE), which can deal with heterogeneous HVACs. Then, a tri-level bidding and dispatching framework is established based on competitive distribution operation with distribution locational marginal price (DLMP). The first two levels form a bilevel model to optimize the aggregators’ payment and to represent the interdependency between load aggregators and the distribution system operator (DSO) using DLMP, and the third level is to dispatch the optimal load aggregation to all residents by the proposed priority list-based demand dispatching algorithm. Finally, case studies on a modified IEEE 33-Bus system illustrate three main technical reasons of payment reduction due to demand flexibility: load shift, DLMP step changes, and power losses. They can be used as general guidelines for better decision-making for future planning and operation of demand response programs.
Article
As the penetration of distributed renewable generation increases, their intrinsic uncertainty and random raise neutral concerns on the reliability of microgrid. Demand side resources are considered helpful in facilitating the operation of microgrid. In recent years, the development of Internet of Things and electric vehicle enables the capability of residential demands serving as flexible grid assets to provide various ancillary services and enhance operation reliability. For the residential flexible resource, their control modes and subsidy prices greatly affect the performance and operation cost, and different resources, such as air conditioner and electric vehicle (distributed energy storage), have considerably different flexible capability, capacity and quantity. Thus, this paper provides an economic-based resource allocation model for residential flexible resources. The proposed model finds the cost-effective plan about how to allocate residential flexible resources to enhance reliability of microgrid. Case studies are conducted on a modified IEEE 9-node network to illustrate the effectiveness of the proposed model.
Chapter
This chapter recalls the load profiling principles and the main differences that appear in applying load profiling to a set of consumers smaller than for classical load profiling conducted at a large-scale level. In the prospect of application to local energy markets, the current load profiling setup is not fully adequate to support the challenging aspects due to the operation of prosumers with local generation and storage. The scopes of load profiling have to be revisited and updated to a more appropriate framework of prosumer profiling. The chapter points out the main objectives of prosumer profiling linked to local energy systems and markets, which include segmentation, forecasting, load balancing, and demand response/flexibility. The load profiling principles are recalled, together with the timing and amplitude aspects relevant to the creation of a comparable set of patterns, including normalization issues and data alignment. Finally, the chapter discusses the needs and opportunities referring to prosumer profiling in local energy markets, also considering multienergy systems, the challenges concerning net power analysis, and the role of uncertainty in the definition of the prosumer profiles.
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Load aggregators (LAs) play a key role in fully tapping the demand response (DR) resources of small and medium-sized end-users to enable a more flexible power grid. In the ancillary service market, the LA can provide DR to the system by aggregating the resources of its users. In response to the issued DR program, end-users offer to provide DR resources. To help optimize the user bidding strategy, an evolutionary game model is presented here in view of the bounded rationality of bidders. A combined Q-learning and compound differential evolution (CDE) algorithm is proposed to deal with the problems of incomplete information and uncertainties in the opponents' decision-making, and prevent the evolutionary stable strategy (ESS) from falling into a local optimum. Moreover, a cloud-computing-based framework is designed and agent servers are introduced to protect data privacy. Numerical results show that by adopting the proposed algorithm, the user's bidding price keeps slightly lower than the opponents' price which guarantees its revenue remains on a high level. This indicates that the proposed algorithm has good adaptability for addressing incomplete information and uncertainties in opponents' decision-making.
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Demand response (DR) is an important technique to explore the demand-side flexibility. The wide deployment of smart meters makes it possible to quantify the baseline load. As an intermediate agent, demand response aggregator needs to obtain the aggregated baseline load (ABL) for the DR event. Previous studies about the household level estimation focus on the estimation method. However, for ABL estimation, customer division is an important issue. A major limitation is the mismatch between the objectives of segmentation and estimation. Therefore, this paper proposes a new closed-loop method for estimating the ABL, which utilizes the contextual bandit with policy gradient to link the segmentation with the estimation. As such, the ABL estimation accuracy can guide the segmentation to divide the customers. The segmentation and estimation optimize collaboratively to improve the ABL estimation accuracy. An ensemble method for combining network’s weights during the training process is proposed. Moreover, a pre-and post-event adjustment method is developed to further improve the estimation accuracy. Comprehensive comparisons demonstrate the proposed method can achieve the best estimation performance with regard to the MAPE and RMSE. It improves the estimation accuracy by 7% in terms of MAPE, and 11% in terms of RMSE.
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In this paper, a new energy management model is proposed to determine the optimal scheduling of an office building which includes electric vehicle (EV) charging piles, batteries, and rooftop photovoltaic systems. To optimally manage the electricity procurement of the building and mitigate the rate of transformer aging, the building energy management system (BEMS) employs the flexibility of batteries and EV charging. In the proposed model, to incentivize EV owners to offer their flexibility, the BEMS organizes a transactive market among plugged-in EVs. To this end, EV owners submit their response curves and the target state-of-charge to the BEMS. Then, the transactive market is cleared to determine the market-clearing price for each EV, the optimal EV charging decisions, and accordingly, the scheduling of office building. Also, to model the correlated uncertainties of solar power generation and demand, the distributionally robust chance-constrained method is employed. Moreover, the “Big-M” technique and the piecewise linear approximation method are utilized to linearize the optimization problem. Finally, the case of a building with 100 charging piles is studied. The numerical results illustrate a decrease in the total operating cost of BEMS and the rate of transformer aging compared to uncontrolled charging and direct control approaches.
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Large-scale wind and solar power integration are likely to cause a short-term mismatch between generation and load demand because of the intermittent nature of the renewables. System frequency is therefore challenged. In recent years, it has been proposed that a part of the residential load can be controlled for frequency regulation with little impact on customer comfort. This paper proposes a thermostatic load control (TLC) strategy for primary and secondary frequency regulation, in particular, using heating, ventilation and air-conditioning (HVAC) units and electric water heaters (EWHs). First, daily demand profile modeling indicates that these two loads are complementary in the daytime and can provide a relatively stable frequency reserve. Second, the progressive load recovery is specifically considered in the control scheme. The random switching and cycle recovery (RS-CR) method is proposed for mitigating power rebound after switching the air conditioners on again. The proposed control strategy can organize a large population of thermostatic loads for the provision of a frequency reserve. Consequently, the requirement of a spinning reserve is reduced. Finally, the proposed control strategy is verified by the dynamic simulation of IEEE RTS 24-bus system.
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In this letter, a novel hybrid component and configuration model is proposed for combined-cycle gas turbines (CCGTs) participating in independent system operator (ISO) markets. In the proposed model, the physical limitations are modelled for each component separately, and the cost is calculated with the bidding curves from the configuration modes. This hybrid mode can represent the current dominant bidding model in the unit commitment problem of ISOs while treating the individual components in CCGTs accurately. The commitment status of the individual components is mapped to the unique configuration mode of the CCGTs. The transitions from one configuration mode to another are also modelled. No additional binary variables are added, and numerical case studies demonstrate the effectiveness of this model in the unit commitment problem.
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Renewable energy based distributed generation (DG) has the potential to reach high penetration levels in the residential region. However, its integration at the demand side will cause rapid power fluctuations of the tie-line in the residential region. The traditional generators are generally difficult to manage rapid power fluctuations due to their insufficient efficiency requirements and low responding speed. With an effective control strategy, the demand side resources (DSRs) including DGs, electric vehicles and thermostatically-controlled loads at the demand side, are able to serve as the energy storage system to smooth the load fluctuations. However, it is a challenge to properly model different types of DSRs. To solve this problem, a unified state model is first developed to describe the characteristics of different DSRs. Then a load curve smoothing strategy is proposed to offset the load fluctuations of the tie-line of the residential region, where a control matrix deduced from the unified state model is introduced to manage the power outputs of different DSRs, considering the response order and the comfort levels. Finally, a residential region with households is used to validate the load curve smoothing strategy based on the unified state model, and the results show that the power fluctuation rate of the tie-line is significantly decreased. Meanwhile, comparative study results are shown to demonstrate the advantages of the unified state model based load curve smoothing strategy.
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The increasing penetration of renewable energy resources brings a number of uncertainties to modern power system operation. In particular, the frequent variation of wind power output causes a short-term mismatch between generation and demand, which causes system frequency fluctuation. The traditional approach to deal with this problem is to increase the amount of system spinning reserve. In recent years, researchers are actively exploring the utilization of residential and commercial loads in frequency regulation without affecting customers’ life quality. This paper first reviews the theoretical basis and application background of the dynamic demand control. Then, the paper summarizes the technical features and advantage/disadvantages of three types of dynamic demand control algorithms, namely centralized control, decentralized control and hybrid control. The technical and economic concerns of this research field are also discussed, which can be future research directions.
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Thermostatically controlled appliances (TCAs) have great thermal storage capability and are therefore excellent demand response (DR) resources to solve the problem of power fluctuation caused by renewable energy. Traditional centralized management is affected by communication quality severely and thus usually has poor real-time control performance. To tackle this problem, a hierarchical and distributed control strategy for TCAs is established. In the proposed control strategy, target assignment has the feature of self-regulating, owing to the designed target assignment and compensating algorithm which can utilize DR resources maximally in the controlled regions and get better control effects. Besides, the model prediction strategy and customers’ responsive behavior model are integrated into the original optimal temperature regulation (OTR-O), and OTR-O will be evolved into improved optimal temperature regulation. A series of case studies have been given to demonstrate the control effectiveness of the proposed control strategy.
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The development of intelligent demand-side management with automatic control enables a large amount of residential demands to provide efficient demand-side ancillary services for load serving entities (LSEs). In this paper, we introduce the concept of a comfort indicator, present an advanced reward system, and finally propose a framework for aggregating residential demands enrolled in incentive-based demand response (DR) pro-grams. The proposed framework not only allocates demand reduction requests (DRRs) among residential appliances quickly and efficiently without affecting residents’ comfort levels but also re-wards residential consumers based on their actual participation. Also, since the framework is designed with the practical considerations of simplicity and efficiency, it can be utilized as a quick implementation for existing pilot development works. The effectiveness and merit of this framework are demonstrated and discussed in the comparison studies with conventional incentive-based DR
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Time-use data, describing in detail the everyday life of household members as high-resolved activity sequences, have a largely unrealized potential of contributing to domestic energy demand modelling. A model for computation of daily electricity and hot-water demand profiles from time-use data was developed, using simple conversion schemes, mean appliance and water-tap data and general daylight availability distributions. Validation against detailed, end-use specific electricity measurements in a small sample of households reveals that the model for household electricity reproduces hourly load patterns with preservation of important qualitative features. The output from the model, when applied to a large data set of time use in Sweden, also shows correspondence to aggregate profiles for both household electricity and hot water from recent Swedish measurement surveys. Deviations on individual household level are predominantly due to occasionally ill-reported time-use data and on aggregate population level due to slightly non-representative samples. Future uses and developments are identified and it is suggested that modelling energy use from time-use data could be an alternative, or a complement, to energy demand measurements in households.
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Load forecasting problems have traditionally been addressed using various statistical methods, among which autoregressive integrated moving average with exogenous inputs (ARIMAX) has gained the most attention as a classical time-series modeling method. Recently, the booming development of deep learning techniques make them promising alternatives to conventional data-driven approaches. While deep learning offers exceptional capability in handling complex non-linear relationships, model complexity and computation efficiency are of concern. A few papers have explored the possibility of applying deep neural networks to forecast time-series load data but only limited to system-level or single-step building-level forecasting. This study, however, aims at filling in the knowledge gap of deep learning-based techniques for day-ahead multi-step load forecasting in commercial buildings. Two classical deep neural network models, namely recurrent neural network (RNN) and convolutional neural network (CNN), have been proposed and formulated under both recursive and direct multi-step manners. Their performances are compared with the Seasonal ARIMAX model with regard to accuracy, computational efficiency, generalizability and robustness. Among all of the investigated deep learning techniques, the gated 24-h CNN model, performed in a direct multi-step manner, proves itself to have the best performance, improving the forecasting accuracy by 22.6% compared to that of the seasonal ARIMAX.
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In China, the continuous increase of peak load has posed a significant challenge to the safety and stability of the power grid. By reasonable control of the air‐conditioning (AC) load, peak load could be reduced, and balance could be achieved between supply and demand at lower cost without affecting customers' comfort. In this paper, a baseline model of the aggregated AC load is introduced and simplified to describe the relationship between temperature setpoint and AC power. The relationship between the electricity price and the temperature setpoint of AC is described based on the consumer psychology theory. Then, a new demand response project called progressive time‐differentiated peak pricing for the AC load is designed. Finally, case study proves the feasibility of the optimal pricing mechanism proposed in this paper. The influences of different price shapes and different compositions of consumers on simulation results are analyzed, providing a theoretical basis and reference data for the practical implementation of progressive time‐differentiated peak pricing.
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The frequency regulation service (FRS) is playing an increasingly important role in maintaining the power balance between generation and consumption. Moreover, the recent progress in information and communication technologies has enabled residential customers to participate in FRS through direct control over appliances, such as inverter air conditioners (ACs), whose market share is growing rapidly and has made up a large fraction of electricity consumption. Inverter ACs can change compressor's speed continuously to adjust operating power and provide FRS for the system operation. In this paper, the thermal model of a room and the electrical model of an inverter AC for providing FRS are developed. The model of the inverter AC is equivalent to a generator. In this manner, the aggregation of inverter ACs can be controlled just as traditional generators. Besides, a stochastic allocation method of the regulation sequence among inverter ACs is proposed to reduce the effect of FRS on customers. A hybrid control strategy by taking into account the dead band control and the hysteresis control is also developed to reduce the frequency fluctuations of power systems. The effectiveness of the proposed models and control strategies are illustrated in the numerical studies.
Article
Residential direct load control (DLC) is an important type of demand response designed to reduce electricity consumption during peak hours through utility companies' control over the operation of certain household appliances. Despite many benefits of DLC, customers' concern for losing control has been hindering its adoption. This study aims to investigate U.S. residents' willingness to accept two popular A/C-related DLC programs in summertime with or without financial incentives or an override option, and to identify the socio-demographic characteristics associated with the decisions. Results of an online survey among 1482 U.S. residents indicate half of the participants are willing to accept DLC without any conditions; however, both an incentive of $30 and an override option boost acceptance rates. Importantly, the override option is more effective than the financial incentive. Residents who are younger, Democrats, non-Whites, have higher education levels, live in larger dwellings, and live with more people are more likely to adopt DLC than their counterparts. Residents who are older, Republicans, Whites, homeowners, and live in a house preferred an override option to financial incentives more often. The implications were discussed in terms of improving power system stability through better DLC program design and implementation.
Article
A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings.
Article
This study introduces an interdisciplinary framework for investigating building-user interaction in office spaces. The framework is a synthesis of theories from building physics and social psychology including social cognitive theory, the theory of planned behavior, and the drivers-needs-actions-systems ontology for energy-related behaviors. The goal of the research framework is to investigate the effects of various behavioral adaptations and building controls (i.e., adjusting thermostats, operating windows, blinds and shades, and switching on/off artificial lights) to determine impacts on occupant comfort and energy-related operational costs in the office environment. This study attempts to expand state-of-the-art understanding of: (1) the environmental, personal, and behavioral drivers motivating occupants to interact with building control systems across four seasons, (2) how occupants’ intention to share controls is influenced by social-psychological variables such as attitudes, subjective norms, and perceived behavioral control in group negotiation dynamic, (3) the perceived ease of usage and knowledge of building technologies, and (4) perceived satisfaction and productivity. To ground the validation of the theoretical framework in diverse office settings and contexts at the international scale, an online survey was designed to collect cross-country responses from office occupants among 14 universities and research centers within the United States, Europe, China, and Australia.
Article
Incentive-based DR Incentive payment Peak and off-peak tariff Price-based DR Price volatility a b s t r a c t There are two general categories of demand response (DR): price-based and incentive-based DR programs. Each one has its own benefits taking advantage of different aspects of flexible demand. In this paper, both categories of DR are modeled based on the demand-price elasticity concept to design an optimum scheme for achieving the maximum benefit of DR. The objective is to not only reduce costs and improve reliability but also to increase customer acceptance of a DR program by limiting price volatility. Time of use (TOU) programs are considered for a price-based scheme designed using a monthly peak and off-peak tariff. For the incentive-based DR, a novel optimization is proposed that in addition to calculation of an adequate and a reasonable amount of load change for the incentive, the best times to realize the DR is found. This optimum threshold maximizes benefit considering the comfort level of customers as a constraint. Results from a reduced model of the WECC show the proposed DR program leads to a significant benefit for both the load serving entities (LSEs) and savings in customer's electricity payment. It also reduces both the average and standard deviation of the monthly locational marginal price (LMP). The proposed DR scheme maintains simplicity for a small customer to follow and for LSEs to implement.
Article
Power flow is the most fundamental computation in power system analysis. Traditionally, the linear solution in power flow is solved by a direct method like LU decomposition on a CPU platform. However, the serial nature of the LU-based direct method is the main obstacle for parallelization and scalability. In contrast, iterative solvers, as an alternative to direct solvers, are generally more scalable with better parallelism. This work presents a fast decouple power flow (FDPF) algorithm with a graphic processing unit (GPU)-based preconditioned conjugate gradient (CG) iterative solver. In addition, the Inexact Newton method is integrated to further improve the GPU-based parallel computing performance for solving FDPF. The results show that the GPU-based FDPF maintains the same precision and convergence as the original CPU-based FDPF, while providing considerable performance improvement for several large-scale systems. The proposed GPU-based FDPF with the Inexact Newton method gives a speedup of 2.86 times for a systemwith over 10,000 buses if compared with traditional FDPF, both implemented based on Matlab. This demonstrates the promising potential of the proposed FDPF computation using a preconditioned iterative solver under GPU architecture.
Conference Paper
Due to the development of intelligent energy management system with automatic control, the large population of residential appliances have the opportunity to be effectively utilized by load serving entities (LSEs) to reduce their operating costs and increase total profit. In practice, LSEs are promoting various demand response (DR) programs to stimulate the flexibility of industrial and commercial demand. However, in the residential sector, due to customers' versatile electricity consumption patterns, fully utilizing the responsive residential demand through DR programs such as incentive based demand response (I-DR) is difficult. Specifically, in I-DR, the most crucial issue for LSEs is how to estimate the residents' potential responses to certain financial incentives. Therefore, this paper presents an approach which integrates three data sets (1. the residential energy consumption survey by the U.S. energy information administration; 2. the American time use survey by the U.S. Department of Labor; and 3. the survey of customers' reactions to financial incentives in DR program by the center for ultra-wide-area resilient electric energy transmission networks) to assess responsive residential demand in a stochastic model. This proposed approach can be easily customized for any given times, locations, financial incentives, and residents' portfolios. Also, it will help LSEs get the valuable insights on regulating the residential demand by adjusting the financial incentives to customers and improving the mechanism existing demand response programs.
Article
Advances in information and communication technologies (ICT) enable a great opportunity to develop the residential demand response that is relevant in smart grid applications. Demand response (DR) aims to manage the required demand to match the available energy resources without adding new generation capacity. Expanding the DR to cover the residential sector in addition to the industrial and commercial sectors gives rise to a wide range of challenges. This study presents an overview of the literature on residential DR systems, load-scheduling techniques, and the latest ICT that supports residential DR applications. Furthermore, challenges are highlighted and analyzed, which are likely to become relevant research topics with regard to the residential DR of smart grid. The literature review shows that most DR schemes suffer from an externality problem that involves the effect of high-level customer consumption on the price rates of other customers, especially during peak period. A recommendation for using adaptive multi-consumption level pricing scheme is presented to overcome this challenge.
Article
Collectively, thermostatically controlled loads (TCLs) offer significant potential for short-term demand response. This intrinsic flexibility can be used to provide various ancillary services or to carry out energy arbitrage. This study introduces an aggregate description of the flexibility of a heterogeneous TCL as a leaky storage unit, with associated constraints that are derived from the TCL device parameters and quality of service requirements. In association with a suitable TCL control strategy this enables a straightforward embedding of TCL dynamics in optimisation frameworks. The tools developed are applied to the problem of determining an optimal multi-service portfolio for TCLs. A linear optimisation model is constructed for the optimal simultaneous allocation of frequency services and energy arbitrage. In a case study, optimal service allocations are computed for eight representative classes of cold appliances and the results are validated using simulations of individual refrigerators. Finally, it is demonstrated that clustering of appliances with similar capabilities can significantly enhance the flexibility available to the system.
Article
PowerShift-Atlantic (PSA) is a pilot project lead by Canadian Maritime utilities that demonstrates direct load control strategies for up to 20 MW of commercial and residential loads for the purpose of balancing the intermittency of renewable generation and supporting demand-side management programs. On the residential side, domestic electric water heaters (DEWHs) form a significant end use class. The ability to accurately estimate and predict the state of individual end use devices allows aggregated control systems to better ensure end-use performance and comfort levels. This paper presents a methodology for estimating and predicting the state of individual DEWHs from models of their thermodynamics and water consumption that are derived under two scenarios: 1) when measurements of both power consumption and water temperature are available; and 2) when only measurements of power consumption are available. The proposed methodology was implemented as part of the PSA pilot project for the DEWH load class to simulate the behavior of the load in presence of the controller and evaluate the performance of the controller. Experimental results show that the model and water usage profile mimic the actual behavior of DEWHs, and can predict the future power consumption when the thermostatic control of a DEWH is interrupted as part of a load control strategy.
Article
Thermostatically controlled loads (TCLs), such as refrigerators, air-conditioners and space heaters, offer significant potential for short-term modulation of their aggregate power consumption. This ability can be used in principle to provide frequency response services, but controlling a multitude of devices to provide a measured collective response has proven to be challenging. Many controller implementations struggle to manage simultaneously the short-term response and the long-term payback, whereas others rely on a real-time command-and-control infrastructure to resolve this issue. In this paper, we propose a novel approach to the control of TCLs that allows for accurate modulation of the aggregate power consumption of a large collection of appliances through stochastic control. By construction, the control scheme is well suited for decentralized implementation, and allows each appliance to enforce strict temperature limits. We also present a particular implementation that results in analytically tractable solutions both for the global response and for the device-level control actions. Computer simulations demonstrate the ability of the controller to modulate the power consumption of a population of heterogeneous appliances according to a reference power profile. Finally, envelope constraints are established for the collective demand response flexibility of a heterogeneous set of TCLs.
Conference Paper
Energy storage devices, such as batteries, have been proposed as a solution to the need for additional power systems services caused by variability and uncertainty in system demand and renewable energy production. However, in many respects, buildings and appliances with thermal mass are equivalent or even superior to batteries for these purposes. In this paper, we examine the potential for residential thermostatically controlled loads (TCLs), such as air conditioners, electric water heaters, and refrigerators, to deliver power systems services and participate in short timescale energy markets. These loads operate within a hysteretic ON/OFF dead-band and therefore act much like energy storage devices, modulating power use around their baseline consumptions. Carefully designed demand response (DR) schemes allow us to both control aggregations of TCLs to track market or automatic generation control signals and ensure that they provide the service requested by the consumer. This paper estimates the size of the potential resource; potential revenue from participation in markets; and break-even costs associated with deploying DR-enabling technologies. We find that current residential TCL energy storage capacity in California is 8–11 GWh, with refrigerators contributing the most. Annual revenues from participation in regulation vary from $10 to $220 per appliance per year depending upon the type of appliance and climate zone, while load following and arbitrage revenues are more modest. We conclude with a number of policy recommendations including the design of new markets and communications/appliance standards that will make it easier to engage residential loads in fast timescale DR.
Article
The data collected by smart meters contain a lot of useful information. One potential use of the data is to track the energy consumptions and operating statuses of major home appliances. The results will enable homeowners to make sound decisions on how to save energy and how to participate in demand response programs. This paper presents a new method to breakdown the total power demand measured by a smart meter to those used by individual appliances. A unique feature of the proposed method is that it utilizes diverse signatures associated with the entire operating window of an appliance for identification. As a result, appliances with complicated middle process can be tracked. A novel appliance registration device and scheme is also proposed to automate the creation of appliance signature database and to eliminate the need of massive training before identification. The software and system have been developed and deployed to real houses in order to verify the proposed method.
Article
This paper investigates the potential of providing aggregated intra-hour load balancing services using heating, ventilating, and air-conditioning (HVAC) systems. A direct-load control algorithm is presented. A temperature-priority-list method is used to dispatch the HVAC loads optimally to maintain consumer-desired indoor temperatures and load diversity. Realistic intra-hour load balancing signals were used to evaluate the operational characteristics of the HVAC load under different outdoor temperature profiles and different indoor temperature settings. The number of HVAC units needed is also investigated. Modeling results suggest that the number of HVACs needed to provide a {+-}1-MW load balancing service 24 hours a day varies significantly with baseline settings, high and low temperature settings, and the outdoor temperatures. The results demonstrate that the intra-hour load balancing service provided by HVAC loads meet the performance requirements and can become a major source of revenue for load-serving entities where the smart grid infrastructure enables direct load control over the HAVC loads.
Chapter
Half-Title PageTitle PageCopyright PageDedication PageTable of ContentsPreface
Article
A solar-assisted HVAC system was retrofitted in 2006–2009 onto an earlier (1980) energy-efficient building. A hybrid system of flat plate and vacuum tube solar collectors heats water in a large hot storage tank that is delivered to an absorption chiller in the cooling season or directly to heating coils in the heating season. Large chilled water storage tanks are charged off-peak and discharged during the day, cooling the building in parallel with the chiller. Measurements of the seasonal performance of the system are presented. Good overall agreement between actual measurements and earlier numerical modeling results is reported for our system, with one notable discrepancy attributable to the operation of the air terminal units, which requires tuning. In cold seasons, solar thermal energy can easily displace a large fraction of traditional heating sources. In the cooling season, the conversion of heat to cooling capacity incurs several parasitic losses, which if not accounted for properly in the design stage, have the capacity to completely offset any advantage gained from the solar system. The economics of building-scale solar thermal systems are strongly dependent on the cost of energy, and electricity in particular. The economics are favorable where electricity costs are high, and vice-versa.
Article
Controlled experiments provided evidence that (1) employees are more likely to accept incentive contracts described in bonus terms than contracts that appear identical except for being described in penalty terms, and (2) when employees' judgment of their past performance is dependent on memory, the preference for bonus over penalty contracts increases with experience. These phenomena are explained in terms of the human information processing costs of communicating and evaluating the contract terms, and further implications are drawn for the empirical study of contracting.
Article
This paper develops new methods to model and control the aggregated power demand from a population of thermostatically controlled loads, with the goal of delivering services such as regulation and load following. Previous work on direct load control focuses primarily on peak load shaving by directly interrupting power to loads. In contrast, the emphasis of this paper is on controlling loads to produce relatively short time scale responses (hourly to sub-hourly), and the control signal is applied by manipulation of temperature set points, possibly via programmable communicating thermostats or advanced metering infrastructure. To this end, the methods developed here leverage the existence of system diversity and use physically-based load models to inform the development of a new theoretical model that accurately predicts – even when the system is not in equilibrium – changes in load resulting from changes in thermostat temperature set points. Insight into the transient dynamics that result from set point changes is developed by deriving a new exact solution to a well-known hybrid state aggregated load model. The eigenvalues of the solution, which depend only on the thermal time constant of the loads under control, are shown to have a strong effect on the accuracy of the model. The paper also shows that load heterogeneity – generally something that must be assumed away in direct load control models – actually has a positive effect on model accuracy. System identification techniques are brought to bear on the problem, and it is shown that identified models perform only marginally better than the theoretical model. The paper concludes by deriving a minimum variance control law, and demonstrates its effectiveness in simulations wherein a population of loads is made to follow the output of a wind plant with very small changes in the nominal thermostat temperature set points.
Article
In this paper, the hourly demand response (DR) is incorporated into security-constrained unit commitment (SCUC) for economic and security purposes. SCUC considers fixed and responsive loads. Unlike fixed hourly loads, responsive loads are modeled with their intertemporal characteristics. The responsive loads linked to hourly market prices can be curtailed or shifted to other operating hours. The study results show that DR could shave the peak load, reduce the system operating cost, reduce fuel consumptions and carbon footprints, and reduce the transmission congestion by reshaping the hourly load profile. Numerical simu- lations in this paper exhibit the effectiveness of the proposed ap- proach.
Article
This paper provides hourly own and cross price elasticities for industrial customers with up to 8 years of experience on Duke Power optional real-time rates. We include the effects of customer characteristics and temperature conditions. Aggregated results show larger own elasticities than have previous studies, complementarity within the potential peak hours and substitution in the late evening. As customers gain experience with hourly pricing, they show larger load reductions during higher priced hours. As compared to a TOU rate, net benefits are $14,000 per customer per month, approximately 4% of the average customer’s bill, and much greater than metering costs. Copyright Springer Science+Business Media, Inc. 2005
Emergency demand response for distribution system contingencies
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Internet, phone, mail and mixed-mode surveys: the tailored design method
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An evaluation of the water heater load potential for providing regulation service
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A probabilistic bottom-up technique for modeling and simulation of residential distributed harmonic sources
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication
1949-3053 (c) 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TSG.2019.2919601, IEEE Transactions on Smart Grid 12