Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources

In the last couple of decades, numerous energy management strategies have been devised to mitigate the effects of greenhouse gas emission, hence introducing the concept of microgrids. In a microgrid, distributed energy generators are used. Microgrid enables a point which ameliorates in exchanging power with the main grid during different times of day. Based on the system constraints, in this work, we aim to efficiently minimize the operating cost of the microgrid and shave the power consumption peaks. For this purpose, we introduce an improved binary bat (iBBat) algorithm which helps to schedule the load demand of smart homes and energy generation from distributed generator of microgrid to the load demand and supply. The proposed energy management algorithm is applied to both grid-connected and islanded modes of the microgrid. The constraints imposed on the algorithm ensure that the load of electricity consumer does not escalate during peak hours. The simulation results are compared with BBat and binary flower pollination algorithm, which validate that the iBBat reflects substantial reduction in operating cost of microgrid. Moreover, results also show a phenomenal reduction in the peak-to-average ratio of load demand from main the main grid. operate in two modes: grid-connected and island modes. In a grid-connected mode it sells the surplus energy to main grid and buys from it when its energy generation is less than the demand. In this mode microgrid is always connected to main grid. On the other hand, an island mode is useful mostly in cases when power supply is interrupted due to the detection of any fault in the grid or in regions such as Russia, where 60% of the territories are not connected to the utility due to their geographical positions [3]. In this case, the connection between microgrid and main grid is terminated.Optimally scheduling the microgrid resources has become a hot research topic from a couple of years. Devising a strategy for managing energy also puts a great impact to optimize the generation pattern of the microgrid in either of its modes. The authors in [4] noticed the large integration of RESs in the microgrid due to which the use of ESS dramatically increased. For this purpose, the authors introduced a bat algorithm to develop corrective strategies to perform least cost dispatches. The authors in [5] employed flower pollination algorithm (FPA) to schedule home appliances to balance the load demand of consumer for demand side management (DSM). Moreover, they put emphasis on the reduction of peak-to-average ratio (PAR) and electricity cost. Zhang et al. [6] introduced a method which helps to schedule the microgrid resources. For this purpose, they proposed a hybrid optimization algorithm. Muhammad et al. [7] introduced an architecture which integrates RESs. A mix-mode energy management strategy is introduced in [8]. It also presents a battery sizing method which helps to operate the microgrid with a minimum operating cost.The power mix generation has a great impact on the cross-country rel...

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“…The parameters of shiftable appliances are given in Table 3. Equation (11) shows the total energy consumed by shiftable appliances in a day [20].…”

Section: Dsm Strategy For Residential Areamentioning

confidence: 99%

“…However, the operating cost of energy supply resources is calculated. The authors in [20], presented an energy management controller using three heuristic…”

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confidence: 99%

An Economical Energy Management Strategy for Viable Microgrid Modes

et al. 2019

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“…Demand Systems analyses how the electric energy is used, their objectives could be one or more from these: low Peak Average Ratio (PAR), minimize costs, minimize consumption and maximizer user´s comfort [11]. To develop DS requires: define objectives, variables and what kind of factors needs to be considered.…”

Section: Introductionmentioning

confidence: 99%

Methodology for the Analysis of Electrical Consumption

Perez-Camacho¹,

González-Calleros²,

Olmos-Pineda³

2019

RCS

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A demand system is designed in order to analyze and control the way in which electricity consumption occurs in a scenario, this type of system has four objectives to reduce consumption, reduce costs, reduce peak average to ratio and maximize the comfort of the users. The design of these systems can be based on the implementation of IoT. When using IoT for the development of demand systems, a layer of information is integrated which generates a multiobjective optimization problem. Metaheuristics are used to solve multiobjective problems. This paper introduces a proposed methodology to develop demand systems and the first products implemented.

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“…2 The energy management solutions are of different types, i.e., direct load reduction, demand response management, and demand side management (DSM) or more specifically residential energy management (REM). [6][7][8][9][10][11][12] The key aspect of DSM or REM solution/algorithms includes, objective identification, constraints satisfaction, problem formulation, and their acceptable implementation. Mathematical modeling of REM affects the complexity of the model directly or indirectly.…”

Section: Introductionmentioning

confidence: 99%

Low complexity approach for energy management in residential buildings

Ahmad

Naeem

Ahmad

2018

Int Trans Electr Energ Syst

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Summary Energy management in residential buildings is one of the major keys for achieving the ambitious goals of efficient energy consumption, minimum carbon footprint, and reduced consumers energy expenditures. In this paper, we propose a novel residential energy management (REM) approach that is different from the conventional approaches. We formulate a REM problem with the objective to maximize the consumers utility under various practical constraints that include human interaction factor, unavailability of power supply, consumers preferences, and priorities. These constraints involve very high number of binary decision variables and result in extremely high search space that renders the solution of the REM problem prohibitively difficult. The application of standard optimization methods to this problem either require huge computational complexity or cannot find its optimal solution. Therefore, to optimally solve this problem, we propose a novel approach where we convert the original problem into an equivalent mathematical model with reduced number of constraints and decision variables. This significantly reduces the solution space of the problem, and standard optimization methods can be used for finding its optimal solution. The simulation conforms that the solution of the reformulated equivalent problem obtains optimal solution to the original REM problem with remarkably reduced computational complexity.

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Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources

Cited by 276 publications

References 14 publications

An Economical Energy Management Strategy for Viable Microgrid Modes

An Economical Energy Management Strategy for Viable Microgrid Modes

Methodology for the Analysis of Electrical Consumption

Low complexity approach for energy management in residential buildings

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