Optimal single phase smart meter design

Mbungu, Nsilulu T.; Naidoo, Vinogran; Bansal, Ramesh C.; Bipath, Minnesh

doi:10.1049/joe.2017.0523

Cited by 22 publications

(27 citation statements)

References 33 publications

(92 reference statements)

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“…Some researchers use the smart metering features to create an energy-efficient system [9], [22]- [24], [135], [154], [166]- [168]. Mbungu et al [26] have proposed an optimal single-phase intelligent metering system that can optimally coordinate the energy flow in the residential sector.…”

Section: B Smart Meteringmentioning

confidence: 99%

“…The DR program has two types which are incentive-based programs (including demand bidding and buyback, direct load control, emergency DR, interruptible load, load as capacity resource, non-SR, regulation service and SR), and time-based programs (including CPP with control, CPP, peak time rebate, RTP, TOU, and system peak response transmission tariff) [226], [227], [241], [242]. It is worth noticing that through smart metering techniques, all time-based DR programs can be designed [24], [26], [155]. The DR strategy offers an opportunity for several sampling intervals for a dynamic control scheme in order to find optimal satisfaction for both supplier and consumer.…”

Section: Demand Side Managementmentioning

confidence: 99%

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Overview of the Optimal Smart Energy Coordination for Microgrid Applications

Mbungu¹,

Naidoo²,

Bansal³

et al. 2019

IEEE Access

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This paper describes an overview of the optimal energy coordination/management approaches for microgrids. The article presents the smart grid environment in conjunction with their technologies into the applications of a microgrid when the energy coordination aims to create power flow stability between the generation and consumption of the electricity. This energy equilibrium is made regardless of a power grid complexity that can contain diverse load demands and distributed energy resources (DERs), including renewable energy system (RES), energy storage system (ESS), electric vehicle (EV), etc. A microgrid often contains an energy mix system that requires three control levels, namely primary, secondary and tertiary, to optimize the energy cost and behavior of the system operation and exploitation. Based on several DERs, a microgrid can operate in island mode or be connected to the main grid. The energy coordination for both features is to deal with the energy resources uncertainty, the climate impact, to reduce atmospheric pollution deriving from the conventional power grid, and the energy demand growth. Through the smart grid technology, the optimization approaches of this coordination have brought several improvements into the electrical system. Thus, an overview of an intelligent energy management system for microgrid applications is intensively detailed to structure the implementation strategies which aim to coordinate the energy flow of an electrical system optimally.

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Section: B Smart Meteringmentioning

confidence: 99%

Section: Demand Side Managementmentioning

confidence: 99%

Overview of the Optimal Smart Energy Coordination for Microgrid Applications

Mbungu¹,

Naidoo²,

Bansal³

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

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“…It is also important to note that flexible load shape strategy could also give the same performance as peak clipping. The main objective of all demand-side management approaches is to induce a dynamic behaviour on electrical loads, which creates a flexibility on the power system that can reduce the cost of energy consumption [9,10].…”

Section: Highlightsmentioning

confidence: 99%

“…In general, real-time electricity pricing is a function of the customer location, weight of consumption, and the type of load [6,7]. Therefore, it can be considered as a time variant function [6] or not [7], and it can be set by both the utility and the consumer for more flexibility of the energy cost [10]. MPC-based energy management system is considered as one of the accurate strategies that can effectively ensure control of the electrical system and reduce the overall cost of energy in real-time in many works.…”

Section: Highlightsmentioning

confidence: 99%

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An optimal energy management system for a commercial building with renewable energy generation under real-time electricity prices

Mbungu

Bansal

Naidoo

et al. 2018

Sustainable Cities and Society

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Highlights• Develops a real-time electricity pricing structure in conjunction with TOU electricity tariff. • Develops an adaptive strategy in the framework of the smart grid using MPC design. • Develops a new approach to optimization of the cost of energy in real-time electricity pricing environment. • Designs an optimal strategy for integrating PV with battery storage to the grid in realtime electricity pricing scheme. • Gives the consumer an opportunity to deal with the cost of electricity usage.Abstract-This paper presents an approach to the energy management and control of the effective cost of energy in real-time electricity pricing environment. The strategy aims to optimise the overall energy flow in the electrical system that minimises the cost of power consumption from the grid. To substantiate these claims different cases of time-of-use (TOU) and renewable energy electricity tariff, i.e. in summer and winter seasons, and the robustness of system is analysed. A given energy demand for commercial usage in the city of Tshwane (South Africa) is used to investigate the behaviour of the designed method during low and high demand periods. As grid integrated renewable energy resources, photovoltaic (PV) is an important consideration in assuring excellent power supply and environmental issues in the commercial building. An adaptive optimal approach in the framework of model predictive control (MPC) is designed to coordinate the energy flow on the electrical system. The results show that the proposed adaptive MPC strategy can promote the new approach of an optimal electrical system design, which reduces the energy cost to pay the utility grid by about 46 % or more depending on the set target. Index Terms-Battery bank, Energy management, Model predictive control, Photovoltaic, Smart grid, Time-of-use tariff. NOMENCLATURE state matrix (-) input matrix (-) cost of electricity to pay or output vector (Rand or R) 1 cost of electricity to pay the utility grid (Rand or R) 2 cost of electricity consumption from the renewable energy (Rand or R) 3 cost of electricity consumption by the load (Rand or R) output matrix (-) 1 input vector (kWh) 1 energy flow of the utility grid (kWh) 2 energy flow of the PV (kWh) 3 energy flow of the charging of battery (kWh) 4 energy flow of the discharging of battery (kWh) energy consumption (kWh) charging energy on battery (kWh) discharging energy from battery (kWh) The deployment of buildings' energy management systems started in the 1970s when the development of direct digital control signal from the advent of a microprocessor was introduced.It offers the ability to control and manage the energy system by providing the users with a betterquality environment. Since its inception, it has been observed that the development of energy Page 3 of 38 energy demand (kWh) energy drawn from battery (kWh) energy consumption at sample (kWh) energy supply from grid (kWh) maximum energy from battery (kWh) energy nominal on battery (kWh) energy supply from photovoltaic array (kWh) PV energy consumption at...

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Energy management system for smart grid: An overview and key issues

2020

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Summary Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug‐in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug‐in electric vehicles, the electricity price, and the time‐varying load bring new challenges to the power engineers to achieve demand‐supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand‐side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting‐edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.

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Optimal single phase smart meter design

Cited by 22 publications

References 33 publications

Overview of the Optimal Smart Energy Coordination for Microgrid Applications

Overview of the Optimal Smart Energy Coordination for Microgrid Applications

An optimal energy management system for a commercial building with renewable energy generation under real-time electricity prices

Energy management system for smart grid: An overview and key issues

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