A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances

There will be a dearth of electrical energy in the world in the future due to exponential increase in electrical energy demand of rapidly growing world population. With the development of Internet of Things (IoT), more smart appliances will be integrated into homes in smart cities that actively participate in the electricity market by demand response programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, the energy management strategy using a price-based demand response program is developed for IoT-enabled residential buildings. We propose a new EMS for smart homes for IoT-enabled residential building smart devices by scheduling to minimize cost of electricity, alleviate peak-to-average ratio, correct power factor, automatic protective appliances, and maximize user comfort. In this method, every home appliance is interfaced with an IoT entity (a data acquisition module) with a specific IP address, which results in a wide wireless system of devices. There are two components of the proposed system: software and hardware. The hardware is composed of a base station unit (BSU) and many terminal units (TUs). The software comprises Wi-Fi network programming as well as system protocol. In this study, a message queue telemetry transportation (MQTT) broker was installed on the boards of BSU and TU. In this paper, we present a low-cost platform for the monitoring and helping decision making about different areas in a neighboring community for efficient management and maintenance, using information and communication technologies. The findings of the experiments demonstrated the feasibility and viability of the proposed method for energy management in various modes. The proposed method increases effective energy utilization, which in turn increases the sustainability of IoT-enabled homes in smart cities. The proposed strategy automatically responds to power factor correction, to protective home appliances, and to price-based demand response programs to combat the major problem of the demand response programs, which is the limitation of consumer’s knowledge to respond upon receiving demand response signals. The schedule controller proposed in this paper achieved an energy saving of 6.347 kWh real power per day, this paper achieved saving 7.282 kWh apparent power per day, and the proposed algorithm in our paper saved $2.3228388 per day.

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Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid

Sensors ◽

10.3390/s20113155 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3155 ◽

Cited By ~ 3

Author(s):

Ghulam Hafeez ◽

Zahid Wadud ◽

Imran Ullah Khan ◽

Imran Khan ◽

Zeeshan Shafiq ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Energy Management ◽

Demand Response ◽

Energy Demand ◽

Smart Cities ◽

Residential Buildings ◽

Electrical Energy ◽

Binary Particle Swarm Optimization ◽

Swarm Optimization ◽

Cost Of Electricity

There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.

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Stochastic electrical energy management of industrial Virtual Power Plant considering time-based and incentive-based Demand Response programs option in contingency condition

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2019-0263 ◽

2020 ◽

Vol 21 (2) ◽

Author(s):

Seyyed Mostafa Nosratabadi ◽

Rahmat-Allah Hooshmand

Keyword(s):

Power Plant ◽

Energy Management ◽

Demand Response ◽

Electrical Energy ◽

Virtual Power ◽

Market Prices ◽

Virtual Power Plant ◽

Heavy Loads ◽

Demand Changes ◽

Demand Response Programs

AbstractNowadays, the sustainable energy management of industrial environments is of great importance because of their heavy loads and behaviors. In this paper, the Virtual Power Plant (VPP) idea is commented as a collected generation to be an appropriate approach for these networks handling. Here, Technical Industrial VPP (TIVPP) is characterized as a dispatching unit contains demands and generations situated in an industrial network. A complete structure is proposed here for possible conditions for different VPPs cooperation in the power market. This structure carries out a day-ahead and intra-day generation planning by choosing the best Demand Response (DR) programs considering wind power and market prices as the uncertain parameters. A risk management study is likewise taken into account in the proposed stages for contingency conditions. So, some component changes, like, regular demand changes and single-line outage are prepared in the framework to authorize the suggested concept in the contingency situation. To determine the adequacy and productivity of the proposed strategy, the IEEE-RTS modified framework is examined to test the technique and to evaluate some reassuring perspectives too. By the proposed methodology, the delectability of DR projects is uncovered in industrial networks and the improvement level of load shedding and the lower cost will be achieved.

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A Critical Assessment of Demand Response Programs Applied for Optimal Energy Management in Microgrids

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1123.0782s719 ◽

2019 ◽

Vol 8 (2S7) ◽

pp. 344-349

Keyword(s):

Energy Management ◽

Demand Response ◽

Power Flow ◽

Renewable Energy Sources ◽

Weather Condition ◽

Optimal Energy ◽

Autonomous Mode ◽

Optimal Energy Management ◽

Demand Response Programs ◽

Microgrid Energy Management

Microgrid Energy Management is done to optimize microgrid performance. Power from Wind Turbines (WT) and Photo Voltaic (PV) modules into a microgrid addresses both factors of environmental concerns as well as sustainable energy production. Point of coupling with utility main grid is disconnected when microgrid functions in autonomous mode and it enhances steady microgrid operation when traditional grids face blackouts. Clean and renewable energy sources being easily affected by variation in weather condition, so taking into account of this uncertainty is essential while formulating power flow problem which can be done through demand response programs. This paper aims to investigate results obtained from research of several researchers scrutinizingly and analyzed critically for optimal energy management in microgrids using demand response programs. This paper also highlights the worthy findings of possible areas of research that would enhance the use of demand side management through demand response programs in microgrids.

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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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System-Theoretic Process Analysis (STPA) for Hazard Analysis in Complex Systems: The Case of “Demand-Side Management in a Smart Grid”

Systems ◽

10.3390/systems8030033 ◽

2020 ◽

Vol 8 (3) ◽

pp. 33 ◽

Cited By ~ 1

Author(s):

Stylianos Karatzas ◽

Athanasios Chassiakos

Keyword(s):

Smart Grid ◽

Complex Systems ◽

Energy Management ◽

Demand Response ◽

Energy Demand ◽

Process Analysis ◽

Hazard Analysis ◽

Energy Market ◽

System Modelling ◽

Home Energy Management

Inelasticity of demand along with the distributed energy sources and energy market democratization pose significant challenges which have considerable negative impacts on overall grid balance. The need for increased capacity and flexibility in the era of energy market digitalization has introduced new requirements in the energy supply network which could not be satisfied without continuous and costly local power network upgrades. Additionally, with the emergence of Smart Homes (SHs) and Home Energy Management (HEM) systems for monitoring and operating household appliances, opportunities have arisen for automated Demand Response (DR). DR is exploited for the modification of the consumer energy demand, in response to the specific conditions within the electricity system (e.g., peak period network congestion). In order to optimally integrate DR in the broader Smart Grid (SG) system, modelling of the system parameters and safety analysis is required. In this paper, the implementation of STPA (System-Theoretic Process Analysis) structured method, as a relatively new hazard analysis technique for complex systems is presented and the feasibility of STPA implementation for loss prevention on a Demand Response system for home energy management, and within the complex SG context, is examined. The applied method delivers a mechanism useful in understanding where gaps in current operational risk structures may exist. The STPA findings in terms of loss scenarios can be used to generate a variety of safeguards to ensure secure operational control and in implementing targeted strategies through standard approaches of risk assessment.

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Installation and Implementation of Energy Management Sytem in the Residential Buildings for Sustainable Development

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c5765.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 2374-2378

Keyword(s):

Energy Management ◽

Dynamic Pricing ◽

Management System ◽

Residential Buildings ◽

Residential Building ◽

Electrical Energy ◽

Rapid Expansion ◽

Energy Management System ◽

Area Network ◽

Load Demand

This paper presents the details of the installation of the energy management system in the buildings of a typical Residential Building Residential Building in India and consequently the reduction of electrical energy consumption. Increasing People intake, the introduction of new courses, research centers and laboratories, and the rapid expansion of Residential Building infrastructure, are rapidly increasing the demand for electricity in universities. Most of the electrical energy consumed in universities in India is for air conditioning and lighting purposes. Established Building Energy Management System (BMS) continuously monitors the electrical load demand of air handling units (AHUs) and room lighting in buildings and reduces the load demand by fitting the AHU thermostats based on the work schedule. One day and on / off control of occupancy based on occupancy in different rooms from remote server rooms via Ethernet. Preliminary findings suggest that a significant reduction in load demand can be achieved in any Residential Building in India through the implementation of BMS, thereby contributing to the reduction of domestic oil consumption. If most universities in the country installed BMS on their Residential Buildinges, it would be possible for electric utilities to use real-time meter data, technology-enabled dynamic pricing, and decisive direct load control for fuel management. There are Residential Buildinges. This paper also highlights the future trend in BMS implementation in universities, as the Residential Building's DSM has a secure Residential Building area network that can effectively use demand response when enabled by high-bandwidth, two-way, end-to-end. Can. Communication in a smart grid environment.

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An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid

Energies ◽

10.3390/en13133299 ◽

2020 ◽

Vol 13 (13) ◽

pp. 3299 ◽

Cited By ~ 1

Author(s):

Mohammad Shakeri ◽

Jagadeesh Pasupuleti ◽

Nowshad Amin ◽

Md. Rokonuzzaman ◽

Foo Wah Low ◽

...

Keyword(s):

Smart Grid ◽

Energy Management ◽

Demand Response ◽

Smart Grids ◽

Management System ◽

Management Systems ◽

Energy Management System ◽

Energy Management Systems ◽

Home Energy Management ◽

Demand Response Programs

Electricity demand is increasing, as a result of increasing consumers in the electricity market. By growing smart technologies such as smart grid and smart energy management systems, customers were given a chance to actively participate in demand response programs (DRPs), and reduce their electricity bills as a result. This study overviews the DRPs and their practices, along with home energy management systems (HEMS) and load management techniques. The paper provides brief literature on HEMS technologies and challenges. The paper is organized in a way to provide some technical information about DRPs and HEMS to help the reader understand different concepts about the smart grid, and be able to compare the essential concerns about the smart grid. The article includes a brief discussion about DRPs and their importance for the future of energy management systems. It is followed by brief literature about smart grids and HEMS, and a home energy management system strategy is also discussed in detail. The literature shows that storage devices have a huge impact on the efficiency and performance of energy management system strategies.

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An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

Inventions ◽

10.3390/inventions4030037 ◽

2019 ◽

Vol 4 (3) ◽

pp. 37 ◽

Cited By ~ 2

Author(s):

Sajad Ghorbani ◽

Rainer Unland ◽

Hassan Shokouhandeh ◽

Ryszard Kowalczyk

Keyword(s):

Energy Management ◽

Demand Response ◽

Energy Demand ◽

Search Algorithm ◽

Renewable Energy Sources ◽

Management Approach ◽

Agent Based ◽

Multi Agent ◽

Power Outputs ◽

Microgrid Energy Management

In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.

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