Energy management of smart micro-grid with response loads and distributed generation considering demand response

Wang, Yongli; Huang, Yujing; Wang, Yudong; Zhang, Ming; Li, Fang; Wang, Yunlu; Zhang, Yuangyuan

doi:10.1016/j.jclepro.2018.06.271

Cited by 173 publications

(75 citation statements)

References 30 publications

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“…Furthermore, in support of the growing interests in the circular economy concept, the smart grid possesses prodigious capacity to accommodate efficient, renewable, and reusable energy solutions to many aspects of the energy grid, e.g., power generation, distribution, and consumption [22]. Many research endeavors have been performed to address the capabilities and opportunities of smart grid in respect to the circular economy framework [18], most notably to address the roles of digital and smart technologies [20,23] and the monitoring of energy generation [51], transmission, distribution [52], consumption [53], and management [50][51][52][53][54] in smart grid scenarios [24].…”

Section: The Smart Grid Industrymentioning

confidence: 99%

Smart Grid R&D Planning Based on Patent Analysis

Ree

Kim

2019

Sustainability

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A smart grid employs information and communications technology to improve the efficiency, reliability, economics, and sustainability of electricity production and distribution. The convergent and complex nature of a smart grid and the multifarious connection between its individual technology components, as well as competition between private companies, which will exert substantial influences on the future smart grid business, make a strategic approach necessary from the beginning of research and development (R&D) planning with collaborations among various research groups and from national, industry, company, and detailed technological levels. However, the strategic, technological, business environmental, and regulatory barriers between various stakeholders with collaborative or sometimes conflictive interests need to be clarified for a breakthrough in the smart grid field. A strategic R&D planning process was developed in this study to accomplish the complicated tasks, which comprises five steps: (i) background research of smart grid industry; (ii) selection of R&D target; (iii) societal, technological, economical, environmental, and political (STEEP) analysis to obtain a macro-level perspective and insight for achieving the selected R&D target; (iv) patent analysis to explore capabilities of the R&D target and to select the entry direction for smart grid industry; and, (v) nine windows and scenario planning analyses to develop a method and process in establishing a future strategic R&D plan. This R&D planning process was further applied to the case of a Korean company holding technological capabilities in the sustainable smart grid domain, as well as in the sustainable electric vehicle charging system, a global consumer market of smart grid. Four plausible scenarios were produced by varying key change agents for the results of this process, such as technology and growth rates, policies and government subsidies, and system standards of the smart grid charging system: Scenario 1, ‘The Stabilized Settlement of the Smart Grid Industry’; Scenario 2, ‘The Short-lived Blue Ocean of the Smart Grid Industry’; Scenario 3, ‘The Questionable Market of the Smart Grid’; and, Scenario 4, ‘The Stalemate of the Smart Grid Industry’. The R&D plan suggestions were arranged for each scenario and detailed ways to cope with dissonant situations were also implied for the company. In sum, in this case study, a future strategic R&D plan was suggested in regard to the electric vehicle charging technology business, which includes smart grid communication system, battery charging duration, service infrastructure, public charge station system, platform and module, wireless charging, data management system, and electric system solution. The strategic R&D planning process of this study can be applicable in various technologies and business fields, because of no inherent dependency on particular subject, like electric vehicle charging technology based on smart grid.

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Section: The Smart Grid Industrymentioning

confidence: 99%

Smart Grid R&D Planning Based on Patent Analysis

Ree

Kim

2019

Sustainability

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“…To resolve these problems, there are multiple options in the conventional grid paradigm such as capacity enhancement of power generation, transmission and distribution infrastructure and renewable energy integration. In contrast, the smart energy grid offers distributed generators (DGs) and demand response (DR) strategies to address these problems [2]. Small scale renewable energy DGs connected to the distribution system may reduce network overloading, generation-demand gap, energy cost, fossil fuel depletion rate and global warming effects due to a decrease in greenhouse gas (GHG) emissions [3].…”

Section: Introductionmentioning

confidence: 99%

Risk-Averse Home Energy Management System

Ali

Malik

Raza³

2020

IEEE Access

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Transformation of conventional energy systems into smart grids enables the integration of residential buildings with distributed generations, electro-thermal storages and demand response policies. Further, it improves the household comfort level and helps to preserve the ecological system. Keeping in view the techno-financial impact of residential energy management, optimal handling of residential customers may prove meaningful for peak load reduction, valley filling, and energy conservation. In this regard, this paper devises a residential energy management system (EMS) to optimally schedule appliances, energy sources and electro-thermal storage for reduction of consumption cost and greenhouse (GHG) emissions. Building integrates national grid, natural gas network and solar energy as input carriers, whereas, electricity, heat and cooling as output carriers. To resolve the risk of loss of load, conditional value at risk (CVaR) has been incorporated in the objective function. Comparison demonstrates that under risk-averse approach, energy retaining capability of electric vehicle and thermal energy storage increases by 28.56% and 53.34%, respectively. This stored energy acts as a reserve during absence of solar irradiance and outages on electric and natural gas networks. Moreover, to make EMS more efficient in tracking optimum solutions with faster convergence speed, a hybrid algorithm has been devised by concatenating the modified flower pollination algorithm with mixed-integer linear programming. The proposed algorithm has been validated by comparing its results with the Salp Swarm Algorithm, Grasshopper Optimization Algorithm, Polar Bear Algorithm, Coyote Optimization and Two Cored Flower Pollination Algorithm (FPA). Results manifest that the cost, GHG emissions and execution time drop by 8.98%, 10.81% and 35.064%, respectively. INDEX TERMS Demand response, energy management, residential buildings, smart grids, stochastic model.

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“…Different operation modes are evaluated according to a two-level management cooperative multi-microgrid MILP-based model for day-ahead scheduling. Towards the application of state of the art, a microgrid energy management a Genetic Algorithm (GA) approach is applied in [48] to optimize cost strategies for scheduling distributed energy resources. The Quasi-static Artificial Bee Colony approach is used to optimize a multi-objective DR problem, based on the cost of energy and peak demand at the building level [49], including PV, Combined Heat and Power (CHP), batteries, electrical energy from the grid, and natural gas.…”

Section: Introductionmentioning

confidence: 99%

Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions

et al. 2018

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Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers to the necessary open and transparent market framework linking energy costs to the actual grid operations. DR allows consumers to directly or indirectly participate in the markets where energy is being exchanged. One of the main challenges for engaging in DR is associated with the initial assessment of the potential rewards and risks under a given pricing scheme. In this paper, a Genetic Algorithm (GA) optimisation model, using Artificial Neural Network (ΑΝΝ) power predictions for day-ahead energy management at the building and district levels, is proposed. Individual building and building group analysis is conducted to evaluate ANN predictions and GA-generated solutions. ANN-based short term electric power forecasting is exploited in predicting day-ahead demand, and form a baseline scenario. GA optimisation is conducted to provide balanced load shifting and cost-of-energy solutions based on two alternate pricing schemes. Results demonstrate the effectiveness of this approach for assessing DR load shifting options based on a Time of Use pricing scheme. Through the analysis of the results, the practical benefits and limitations of the proposed approach are addressed.

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Energy management of smart micro-grid with response loads and distributed generation considering demand response

Cited by 173 publications

References 30 publications

Smart Grid R&D Planning Based on Patent Analysis

Smart Grid R&D Planning Based on Patent Analysis

Risk-Averse Home Energy Management System

Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions

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