Power Scheduling Scheme for DSM in Smart Homes with Photovoltaic and Energy Storage

Zator, S.

doi:10.3390/en14248571

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…Mohammad et al [24] propose an EMS for residential buildings with onsite PV, energy storage, and electric vehicles utilized as another storage system while minimizing the operating cost, peak to average ratio, and the user discomfort (including the electric vehicles' availability). Zator [25] presents a case study to measure the impact of the utilization of an EMS for homes with PV and heat and electric energy storage system installations on self-consumption of a single-family house with price-based DSM. Kumar et al [26] propose a method to integrate customer-oriented and utility-oriented DSM policies into microgrids' EMS, considering non-dispatchable energy resources.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, if bus i represents a microgrid, the DNO may request a load reduction of q m,k,t units of electricity at a particular period t. If the DNO is still unable to satisfy that demand, it is possible to incur load shedding of s i,k,t units of electricity. The amount of electricity sold to bus i after the above-mentioned considerations is modeled in Equations ( 23)- (25).…”

Section: Demand Responsementioning

confidence: 99%

“…The maximum amount that can be requested to be reduced from microgrids at each DT level is modeled as Q m,k,t in (24). The load that can be shed from a certain bus cannot be greater than the demand of this bus, as modeled in (25).…”

Section: Demand Responsementioning

confidence: 99%

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Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies

Duarte

Fan

2022

Energies

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The international community has set ambitious targets to replace the use of fossil fuels for electricity generation with renewable energy sources. The use of large-scale (e.g., solar farms) and small-scale solutions (e.g., onsite green technologies) represents one way to achieve these goals. This paper presents a mathematical optimization framework to coordinate the energy decisions between the distribution network and the networked microgrids embedded within it. Utility-scale renewable and conventional generators are considered in the distribution network, while the microgrids include onsite renewable generation and energy storage. The distribution network operator utilizes demand-side management policies to improve the network’s efficiency, and the microgrids operate under these programs by reducing their energy usage, scheduling the electricity usage under dynamic tariffs, and supplying energy to the grid. The uncertainty of renewable energy sources is addressed by robust optimization. The decisions of the distribution network and the microgrids are made independently, whereas the proposed collaboration scheme allows for the alignment of the systems’ objectives. A case study is analyzed to show the capability of the model to assess multiple configurations, eliminating the necessity of load shedding, and increasing the power supplied by the microgrids (22.3 MW) and the renewable energy share by up to 5.03%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Demand Responsementioning

confidence: 99%

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Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies

Duarte

Fan

2022

Energies

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“…In addition, the residents communicate their preferences and reveal private information about their lifestyle. In a highly insulated building, a control system controls the heating [6], charging of electric vehicles [7], hot water supply, electrical storage [8] and other electrical devices on the basis of these data. The high level of insulation in the building requires a ventilation system with a heat exchanger to maintain the indoor air quality.…”

Section: What Can An Active Building Do?mentioning

confidence: 99%

“…This will enable us, in the next section, to assign the supply chains to organizations or firms, each with their own business model. We do not consider the use of electricity storage with the specific aim of maximizing self-consumption of renewable power generated by the building [8]. This service may well be valued by the building's occupants; it may also be encouraged by electricity tariffs that recover the (largely fixed) costs of the grid through charges per unit of electricity sold that are greater than the marginal cost of power (which has been the case (in most hours) in the UK).…”

Section: Value Proposition Of Active Buildingsmentioning

confidence: 99%

Business Models for Active Buildings

2022

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Active Buildings that allow users to adjust their demands on the grid to the needs of the energy system could greatly assist the transition to net zero, but will not be widely adopted unless the businesses involved can make money from doing so. We describe the construction, flexibility and information supply chains of activities needed to make these buildings work. Drawing on the results of an expert workshop, we set out four possible business models deserving further investigation. Developers may find it profitable to build or upgrade energy-efficient buildings with the monitoring and control equipment needed to adjust demand and energy storage as required, selling them soon after completion. Aggregators monitor the state of the building and communicate with the energy system to adjust the building’s demand while maintaining comfort levels, in return for suitable payments. Energy service companies may sell energy-as-a-service and own the equipment instead of a consumer who wishes to minimize their upfront costs, and the idea of an active, energy-efficient, building may be attractive to the tenants of the new group of all-inclusive rental companies, and hence to those companies. Our discussion shows that each is an evolution of an existing (successful) business model, but that further work will be needed to evaluate their profitability when applied to Active Buildings.

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An Industrial Load Classification Method Based on a Two-Stage Feature Selection Strategy and an Improved MPA-KELM Classifier: A Chinese Cement Plant Case

Zhou

Zhu

et al. 2023

Electronics

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Accurately identifying industrial loads helps to accelerate the construction of new power systems and is crucial to today’s smart grid development. Therefore, this paper proposes an industrial load classification method based on two-stage feature selection combined with an improved marine predator algorithm (IMPA)-optimized kernel extreme learning machine (KELM). First, the time- and frequency-domain features of electrical equipment (active and reactive power) are extracted from the power data after data cleaning, and the initial feature pool is established. Next, a two-stage feature selection algorithm is proposed to generate the smallest features, leading to superior classification accuracy. In the initial selection phase, each feature weight is calculated using ReliefF technology, and the features with smaller weights are removed to obtain the candidate feature set. In the reselection stage, the k-nearest neighbor classifier (KNN) based on the MPA is designed to obtain the superior combination of features from the candidate feature set concerning the classification accuracy and the number of feature inputs. Third, the IMPA-KELM classifier is developed as a load identification model. The MPA improvement strategy includes self-mapping to generate chaotic sequence initialization and boundary mutation operations. Compared with the MPA, IMPA has a faster convergence speed and more robust global search capability. In this paper, actual data from the cement industry within China are used as a research case. The experimental results show that after two-stage feature selection, the initial feature set reduces the feature dimensionality from 58 dimensions to 3 dimensions, which is 5.17% of the original. In addition, the proposed IMPA-KELM has the highest overall recognition accuracy of 93.39% compared to the other models. The effectiveness and feasibility of the proposed method are demonstrated.

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Power Scheduling Scheme for DSM in Smart Homes with Photovoltaic and Energy Storage

Cited by 4 publications

References 26 publications

Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies

Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies

Business Models for Active Buildings

An Industrial Load Classification Method Based on a Two-Stage Feature Selection Strategy and an Improved MPA-KELM Classifier: A Chinese Cement Plant Case

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