Multi-Objective Optimization Model of Source–Load–Storage Synergetic Dispatch for a Building Energy Management System Based on TOU Price Demand Response

Wang, Fei; Zhou, Lidong; Ren, Hui; Liu, Xiaoli; Talari, Saber; Shafie‐khah, Miadreza; Catalào, João P. S.

doi:10.1109/tia.2017.2781639

Cited by 210 publications

(70 citation statements)

References 32 publications

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“…In addition, in order to better maintain the balance between supply and demand in power system, demand response (DR), which can be classified into two categories: price-based and incentive-based, has emerged as a tool to engage in the grid operation [25,26] and improve the flexibility of power system [27]. It is the most important task to estimate the demand response capacity (DRC) of all participants before the implementing process of DR programs and the customer baseline load after that.…”

Section: Introductionmentioning

confidence: 99%

Impact Analysis of Customized Feedback Interventions on Residential Electricity Load Consumption Behavior for Demand Response

Wang

Liu

et al. 2018

Energies

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Considering the limitations of traditional energy-saving policies, a kind of energy conservation method called the Information Feedback to Residential Electricity Load Customers, which could impact the demand response capacity, has increasingly received more attention. However, most of the current feedback programs provide the same feedback information to all customers regardless of their diverse characteristics, which may reduce the energy-saving effects or even backfire. This paper attempts to investigate how different types of customers may change their behaviors under a set of customized feedback. We conducted a field survey study in Qinhuangdao (QHD), China. First, we conducted semi-structured interviews to classify four groups of customers of different energy-saving awareness, energy-saving potential, and behavioral variability. Then, 156 QHD households were surveyed using scenarios to collect feedback of different scenarios. Social science theories were used to guide the discussion on the behavior changes as a result of different feedback strategies and reveal the reasons for customers' behaviors. Using the Chi-Square test of independence, the variables that have strong correlations with the categories of residents are extracted to provide references for residents' classification. Finally, the practical implications and needs for future research are discussed.

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Section: Introductionmentioning

confidence: 99%

Impact Analysis of Customized Feedback Interventions on Residential Electricity Load Consumption Behavior for Demand Response

Wang

Liu

et al. 2018

Energies

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“…In [18], Basit et al have proposed a home EMS aiming at optimizing the operation cost by scheduling household appliances without violating the required operation duration of non-schedulable devices. A day-ahead multi-objective optimization model for building energy management has been proposed in [19]. Based on the forecasted information on PV generation, load demand, and temperature, the synergetic dispatch of source-load-storage is scheduled to minimize the operation cost under ToU energy pricing while maintaining the users' comfort.…”

Section: Energy Management In Smart Energy Buildingmentioning

confidence: 99%

“…These smart energy buildings can be considered as a type of microgrid systems since they can operate in island-mode with the help of ESS, RES, or V2G systems. The literatures of [14][15][16][17][18][19][20][21] aimed at optimizing the energy consumptions or minimizing operation costs by flexibly and intelligently managing the various kinds of controllable energy sources and loads such as ESS, RES, and V2G system in the smart energy building.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

2018

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A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a smart grid environment is increasing. In this paper, we consider an energy management system for a smart energy building connected to an external grid (utility) as well as distributed energy resources including a renewable energy source, energy storage system, and vehicle-to-grid station. First, the energy management system is modeled using a Markov decision process that completely describes the state, action, transition probability, and rewards of the system. Subsequently, a reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown future information. The results of numerical simulation based on the data measured in real environments show that the proposed energy management algorithm gradually reduces energy costs via learning processes compared to other random and non-learning-based algorithms.

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“…Correspondingly, DSM programs can be divided into two basic categories, i.e., price-based DSM, and incentive-based DSM. In price-based DSM, the end-use customers would adjust its demand based on the time-varying price, which has been discussed in many literatures [3][4][5][6][7][8]. In incentive-based DSM programs, end-use customers act as controllable loads dispatched by external signals.…”

Section: Sumentioning

confidence: 99%

Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing Approach

Ding

Zhu

Wang

et al. 2019

2019 IEEE/IAS 55th Industrial and Commercial Power Systems Technical Conference (I&CPS)

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With the rapidly increased penetration of renewable generations, incentive-based demand side management (DSM) shows great value on alleviating the uncertainty and providing flexibility for microgrid. However, how to price those demand resources becomes one of the most significant challenges for promoting incentive-based DSM under microgrid environments. In this paper, a flexible demand resource pricing scheme is proposed. Instead of using the utility function of end users like most existing literatures, the economic benefit of flexible demand resources is evaluated by the operation performance enhancement of microgrid and correspondingly the resource is priced based on a benefit sharing approach. An iteration-based chance-constrained method is established to calculate the economic benefit and shared compensation for demand resource providers. Meanwhile, the financial risks for the microgrid operator due to uncertain factors are mitigated by the chanceconstrained criterion. The proposed scheme is examined by an experimental microgrid to illustrate its effectiveness.Index Terms-Demand side management, resource pricing, renewable energy integration, microgrid, risk management

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Multi-Objective Optimization Model of Source–Load–Storage Synergetic Dispatch for a Building Energy Management System Based on TOU Price Demand Response

Cited by 210 publications

References 32 publications

Impact Analysis of Customized Feedback Interventions on Residential Electricity Load Consumption Behavior for Demand Response

Impact Analysis of Customized Feedback Interventions on Residential Electricity Load Consumption Behavior for Demand Response

Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing Approach

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