Energy management in microgrid using incentive-based demand response and reconfigured network considering uncertainties in renewable energy sources

The current lifestyle of humanity relies heavily on energy consumption, thus rendering it an inevitable need. An ever-increasing demand for energy has resulted from the increasing population. Most of this demand is met by the traditional sources that continuously deplete and raise significant environmental issues. The existing power structure of developing nations is aging, unstable, and unfeasible, further prolonging the problem. The existing electricity grid is unstable, vulnerable to blackouts and disruption, has high transmission losses, low quality of power, insufficient electricity supply, and discourages distributed energy sources from being incorporated. Mitigating these problems requires a complete redesign of the system of power distribution. The modernization of the electric grid, i.e., the smart grid, is an emerging combination of different technologies designed to bring about the electrical power grid that is changing dramatically. Demand side management (DSM) allow customers to be more involved in contributors to the power systems to achieve system goals by scheduling their shiftable load. Effective DSM systems require the participation of customers in the system that can be done in a fair system. This paper focuses primarily on techniques of DSM and demand responses (DR), including scheduling approaches and strategies for optimal savings.

show abstract

“…The residential customer should shift/curtail load for a specific amount/time. Emergency [19] Incentivebased…”

Section: Utilitymentioning

confidence: 99%

Various demand side management techniques and its role in smart grid–the state of art

Muthuselvi

Balasubramanian²

2022

IJECE

View full text Add to dashboard Cite

show abstract

“…The terms a lin , b lin , a pwr , b pwr , a exp , b exp , a log , b log , a hyp , and b hyp represents the constant values for load models. The flexible self-and cross-elasticity matrices for linear and nonlinear load-responsive models are defined in ( 17) to (26). Finally, the flexible load models ( 27) to (31) with modified price elasticity matrices are derived based on "customer benefit function" and "price elasticity."…”

Section: Responsive Load Economic Modelmentioning

confidence: 99%

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

Singh

Ding

Raju

et al. 2021

Intl J of Energy Research

View full text Add to dashboard Cite

Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.

show abstract

“…16 Harsh and Das proposed a UC incorporating the demand response program in a reconfigured power network. 17 The purpose of the study is to reduce the fuel cost of the conventional DGs and the purchasing cost of power from the grid. The uncertainty of RESs is modelled using the point estimation method whereas the uncertainty of other parameters is neglected.…”

Section: Introductionmentioning

confidence: 99%

“…Anand suggested a multi‐objective thermal unit based UC by utilizing both the binary and continuous PSO algorithms 16 . Harsh and Das proposed a UC incorporating the demand response program in a reconfigured power network 17 . The purpose of the study is to reduce the fuel cost of the conventional DGs and the purchasing cost of power from the grid.…”

Section: Introductionmentioning

confidence: 99%

Dual stage microgrid energy resource optimization strategy considering renewable and battery storage systems

Islam

Nagrial

Rizk

et al. 2021

Intl J of Energy Research

View full text Add to dashboard Cite

Optimal operation of micro grids (MGs) with high penetration of renewable energy sources (RESs) has become inflexible as a consequence of the uncertainty and intermittency nature of RESs. Energy management along with battery energy storage system (BESS) is thus essential to minimize these uncertainties of MGs and to ensure continuity of energy supply and maintain the reliability of the system. In this paper, a dual-stage MG optimal energy management approach considering the BESS model and other dispatchable resources is introduced to minimize the operating cost. The proposed approach comprises of the schedule and dispatch stages. The mixed-integer linear programming is applied to solve the dual-stage energy management problem and the performance of the suggested technique is compared with the particle swarm optimization technique. The obtained results prove that the incorporation of the suggested BESS plant can significantly reduce the operating costs up to 7% and 6% when committed to supplying power in the scheduling and dispatch stages. The results also show that power loss has a considerable impact on MG energy management. The optimal simulation results confirm the effectiveness of the proposed microgrid energy resources optimization strategy.

show abstract

Energy management in microgrid using incentive-based demand response and reconfigured network considering uncertainties in renewable energy sources

Cited by 54 publications

References 69 publications

Various demand side management techniques and its role in smart grid–the state of art

Various demand side management techniques and its role in smart grid–the state of art

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

Dual stage microgrid energy resource optimization strategy considering renewable and battery storage systems

Contact Info

Product

Resources

About