A Multiagent-Based Game-Theoretic and Optimization Approach for Market Operation of Multimicrogrid Systems

Esfahani, Mohammad Mahmoudian; Hariri, Abla O.; Mohammed, Osama A.

doi:10.1109/tii.2018.2808183

Cited by 94 publications

(58 citation statements)

References 25 publications

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“…Online energy management was proposed in [3], [18], [19] based on distributed algorithms to optimize their internal power devices and external energy trading with the electricity market and other microgrids. In [7], [20], [21], the game theory is used to introduce an incentive mechanism to encourage transactive energy trading and fair benefit sharing. However, so far those previous works focused only on systems with a single energy carrier.…”

Section: B Literature Reviewmentioning

confidence: 99%

Distributed Multienergy Coordination of Multimicrogrids With Biogas-Solar-Wind Renewables

Zhou

Chan

et al. 2019

IEEE Trans. Ind. Inf.

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Abstract-This paper proposes a distributed multi-energy management framework for the coordinated operation of interconnected biogas-solar-wind microgrids. In this framework, each microgrid not only schedules its local hybrid biogas-solar-wind renewables for coupled multi-carrier energy supplies based on the concept of energy hub, but also exchanges energy with interconnected microgrids and via the transactive market. The multi-microgrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multi-microgrid multi-energy coupling matrix is thus formulated to model and exploit the inherent biogas-solar-wind energy couplings among electricity, gas and heat flows. Furthermore, a distributed stochastic optimal scheduling scheme with minimum information exchange overhead is proposed to dynamically optimize energy conversion and storage devices in the multi-microgrid system. The proposed method has been fully tested and benchmarked on different scaled multi-microgrid system over a 24-hour scheduling horizon. Comparative results demonstrated that the proposed approach can reduce the system operating cost and enhance the system energy-efficiency, and also confirm its scalability in solving large-scale multi-microgrid problems.

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Section: B Literature Reviewmentioning

confidence: 99%

Distributed Multienergy Coordination of Multimicrogrids With Biogas-Solar-Wind Renewables

Zhou

Chan

et al. 2019

IEEE Trans. Ind. Inf.

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“…Γ 1 1 (13) where and , are the mean wind speed and its variance, respectively. These parameters are calculated based on historical data for every interval.…”

Section: Wind Turbine (Wt) System Modelmentioning

confidence: 99%

“…There have been numerous studies utilizing benefits of MAS-based solution approaches in control and energy management of microgrids [11,12]. A MAS-based game theoretic optimization approach consisting of a double-auction mechanism for the day-ahead market and reverse auction model for the hour-ahead and real-time markets is proposed in [13]. In [14], a game theoretic non-cooperative distributed coordination control (NCDCC) scheme is suggested, trying to address multi-operator energy trading for microgrids.…”

mentioning

confidence: 99%

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

et al. 2019

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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.The major share of the energy production in microgrids usually comes from renewable energy sources such as photovoltaic panels (PV) or wind turbines (WT). The intermittent nature of such units can cause fluctuation and power imbalance in the system. Hence, energy management in microgrids considering the uncertainties in generation/demand has always been of paramount importance to researchers in this field [3-6]. State-of-the-ArtResearch on all aspects of microgrids has become very popular in the recent past. In this review we will concentrate on optimization objectives in microgrids only since this is the topic of this paper.Reference [3] presents a review of existing optimization objectives, tools, and solution approaches used in microgrid energy management. In [6], a summary of various uncertainty quantification methods along with a comparative analysis on utilized communication technologies are presented. An energy management solution based on a Bayesian optimization algorithm (BOA) is proposed by [7] in which the optimization problem is formulated without a closed-form objective function expression, and solved using a BOA-based data-driven framework. In [8], a two-layer predictive energy management system (EMS) for microgrids was proposed. The upper layer reduces the total operational cost and the lower layer try to remove the forecast fluctuations. In [9], an optimal scheduling of a standalone microgrid under system uncertainties is proposed. This model uses a dynamic programming method to solve a single-objective optimization problem to reduce the operation and emission costs.Flexibility and cooperative behavior of multi-agent systems (MASs) make them appropriate candidates for ener...

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“…Specifically, probabilistic programming models have been proposed in [12,13], while dynamic programming models are suggested in [14,15]. These dynamic and stochastic methods require fine-tuning in their algorithm-specific control parameters.…”

Section: Energy Management Using Deterministic and Chance-constrainedmentioning

confidence: 99%

“…Dynamic programming models [14,15] Residential and industrial MG. Inefficient for large decision variables.…”

mentioning

confidence: 99%

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

et al. 2018

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This paper presents a model for optimal energy management under the time-of-use (ToU) and critical peak price (CPP) market in a microgrid. The microgrid consists of intermittent dispatchable distributed generators, energy storage systems, and multi-home load demands. The optimal energy management problem is a challenging task due to the inherent stochastic behavior of the renewable energy resources. In the past, medium-sized distributed energy resource generation was injected into the main grid with no feasible control mechanism to prevent the waste of power generated by a distributed energy resource which has no control mechanism, especially when the grid power limit is altered. Thus, a Jaya-based optimization method is proposed to shift dispatchable distributed generators within the ToU and CPP scheduling horizon. The proposed model coordinates the power supply of the microgrid components, and trades with the main grid to reduce its fuel costs, production costs, and also maximize the monetary profit from sales revenue. The proposed method is implemented on two microgrid operations: the standalone and grid-connected modes. The simulation results are compared with other optimization methods: enhanced differential evolution (EDE) and strawberry algorithm (SBA). Finally, simulation results show that the Jaya-based optimization method minimizes the fuel cost by up to 38.13%, production cost by up to 93.89%, and yields a monetary benefit of up to 72.78% from sales revenue.

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A Multiagent-Based Game-Theoretic and Optimization Approach for Market Operation of Multimicrogrid Systems

Cited by 94 publications

References 25 publications

Distributed Multienergy Coordination of Multimicrogrids With Biogas-Solar-Wind Renewables

Distributed Multienergy Coordination of Multimicrogrids With Biogas-Solar-Wind Renewables

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

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

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