Energy Demand Management and Demand Response

Acting to preserve our planet as much as possible is no longer optional in today's world. To do so, Smart Grids within the framework of electrical networks - involving not only Distribution System Operators (DSOs), but also consumers in their Energy Demand Management (EDM) activity - represent an innovative and sustainable solution. However, the integration of Smart Grids into network management or into consumers' homes implies changes at several levels: organizational, social, psychological, etc. This is why it is essential to consider the human factor in the design of the technologies used in these Smart Grids. This paper proposes the integration of DSO operators and consumers within a user-centered evaluation approach in order to design Smart Grids that are sufficiently acceptable to users to enable Positive Energy Territories that produce more energy than they consume. This demonstration will be illustrated by the VERTPOM® project aiming at facilitating the use of renewable energies specific to each territory in order to contribute to the reduction of greenhouse gases and make the territories less dependent on traditional energies, and thus make Picardy (in France) a Positive Energy Territory. This paper presents the user-centered evaluation approach applied to three technologies (i.e., the VERTPOM-BANK® supervision tool intended for DSO operators, the private web portal and the IBox smart meter intended for households) from the upstream design phase to the implementation of the technologies in real-life situations.

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Revealing the superpowers of smart meters for water-energy demand management and behaviour change

10.1002/essoar.10501369.1 ◽

2019 ◽

Author(s):

Cara Beal

Keyword(s):

Behaviour Change ◽

Energy Demand ◽

Demand Management ◽

Smart Meters

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An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids

Energy & Environment ◽

10.1177/0958305x18787259 ◽

2018 ◽

Vol 30 (1) ◽

pp. 63-80 ◽

Cited By ~ 10

Author(s):

Paraskevas Panagiotidis ◽

Andrew Effraimis ◽

George A Xydis

Keyword(s):

Demand Response ◽

Energy Demand ◽

Moving Average ◽

Electricity Consumption ◽

End Users ◽

Market Model ◽

Residential Sector ◽

Response Strategies ◽

System Operator ◽

Modelling Methodology

The main aim of this work is to reduce electricity consumption for consumers with an emphasis on the residential sector in periods of increased demand. Efforts are focused on creating a methodology in order to statistically analyse energy demand data and come up with forecasting methodology/pattern that will allow end-users to organize their consumption. This research presents an evaluation of potential Demand Response programmes in Greek households, in a real-time pricing market model through the use of a forecasting methodology. Long-term Demand Side Management programs or Demand Response strategies allow end-users to control their consumption based on the bidirectional communication with the system operator, improving not only the efficiency of the system but more importantly, the residential sector-associated costs from the end-users’ side. The demand load data were analysed and categorised in order to form profiles and better understand the consumption patterns. Different methods were tested in order to come up with the optimal result. The Auto Regressive Integrated Moving Average modelling methodology was selected in order to ensure forecasts production on load demand with the maximum accuracy.

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Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Energies ◽

10.3390/en13164154 ◽

2020 ◽

Vol 13 (16) ◽

pp. 4154 ◽

Cited By ~ 4

Author(s):

Anthony Faustine ◽

Lucas Pereira

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Energy Demand ◽

Learning Strategy ◽

Demand Management ◽

Management Strategies ◽

Electricity Consumption ◽

Consumption Pattern ◽

Similarity Function ◽

Active Components

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

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A Model for Multi-Energy Demand Response with Its Application in Optimal TOU Price

Energies ◽

10.3390/en12060994 ◽

2019 ◽

Vol 12 (6) ◽

pp. 994 ◽

Cited By ~ 1

Author(s):

Nan Zhao ◽

Beibei Wang ◽

Mingshen Wang

Keyword(s):

Demand Response ◽

Energy Demand ◽

Alternative Energy ◽

Energy System ◽

Response Characteristic ◽

Test System ◽

Interior Point Algorithm ◽

Response Model ◽

Transfer Energy ◽

Typical Application

With the generalization of the integrated energy system (IES) on the demand side, multi-energy users may participate in a demand response (DR) program based on their flexible consumption of energy. However, since users could choose using alternative energy or transfer energy consumption to other time periods, obtaining response characteristics of this type of DR usually appears more complicated than traditional single-energy DR. To obtain the response characteristic, a response model for multi-energy DR, which reflects the relations between electricity (gas) response and time-of-use (TOU) electric prices, is proposed. The model is characterized by several coefficients which are associated with electric and heat efficiency. The model is obtained through the derivation process of optimizing user’s energy-using problem. Then, as a typical application of the response model, the TOU electric pricing for multi-energy users is able to be formulated by an interior point algorithm after giving the Kuhn-Tucker conditions of the optimal problem. Typical results of the optimal TOU pricing are further illustrated through the formulation on a PJM five-bus test system. It demonstrates that optimal TOU pricing can be effectively pre-calculated by the utility company using the proposed response model.

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Futuristic Sustainable Energy Management in Smart Environments: A Review of Peak Load Shaving and Demand Response Strategies, Challenges, and Opportunities

Sustainability ◽

10.3390/su12145561 ◽

2020 ◽

Vol 12 (14) ◽

pp. 5561 ◽

Cited By ~ 4

Author(s):

Bhagya Nathali Silva ◽

Murad Khan ◽

Kijun Han

Keyword(s):

Energy Management ◽

Demand Response ◽

Energy Demand ◽

Management Strategies ◽

Peak Load ◽

Critical Discussion ◽

Future Research ◽

Smart Environments ◽

Challenges And Opportunities ◽

Management Approaches

The emergence of the Internet of Things (IoT) notion pioneered the implementation of various smart environments. Smart environments intelligibly accommodate inhabitants’ requirements. With rapid resource shrinkage, energy management has recently become an essential concern for all smart environments. Energy management aims to assure ecosystem sustainability, while benefiting both consumers and utility providers. Although energy management emerged as a solution that addresses challenges that arise with increasing energy demand and resource deterioration, further evolution and expansion are hindered due to technological, economical, and social barriers. This review aggregates energy management approaches in smart environments and extensively reviews a variety of recent literature reports on peak load shaving and demand response. Significant benefits and challenges of these energy management strategies were identified through the literature survey. Finally, a critical discussion summarizing trends and opportunities is given as a thread for future research.

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Research on Economic Operation Strategy of CHP Microgrid Considering Renewable Energy Sources and Integrated Energy Demand Response

Sustainability ◽

10.3390/su11184825 ◽

2019 ◽

Vol 11 (18) ◽

pp. 4825 ◽

Cited By ~ 1

Author(s):

Jun Dong ◽

Shilin Nie ◽

Hui Huang ◽

Peiwen Yang ◽

Anyuan Fu ◽

...

Keyword(s):

Renewable Energy ◽

Demand Response ◽

Energy Demand ◽

Renewable Energy Sources ◽

Rapid Development ◽

Utilization Efficiency ◽

Energy Output ◽

Mixed Integer ◽

Energy Structure ◽

Economic Operation

Renewable energy resources (RESs) play an important role in the upgrading and transformation of the global energy structure. However, the question of how to improve the utilization efficiency of RESs and reduce greenhouse gas emissions is still a challenge. Combined heating and power (CHP) is one effective solution and has experienced rapid development. Nevertheless, with the large scale of RESs penetrating into the power system, CHP microgrid economic operation faces great challenges. This paper proposes a CHP microgrid system that contains renewable energy with considering economy, the environment, and system flexibility, and the ultimate goal is to minimize system operation cost and carbon dioxide emissions (CO2) cost. Due to the volatility of renewable energy output, the fuzzy C-means (FCM) and clustering comprehensive quality (CCQ) models were first introduced to generate clustering scenarios of the renewable energy output and evaluate the clustering results. In addition, for the sake of improving the flexibility and reliability of the CHP microgrid, this paper considers the battery and integrated energy demand response (IEDR). Moreover, the strategy choices of microgrid operators under the condition of grid-connected and islanded based on environment and interest aspects are also developed, which have rarely been involved in previous studies. Finally, this stochastic optimization problem is transformed into a mixed integer linear programming (MILP), which simplifies the calculation process, and the results show that the operation mode under different conditions will have a great impact on microgrid economic and environmental benefits.

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