A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids

Liu, Nian; Tang, Qingfeng; Zhang, Jianhua; Fan, Wei; Liu, Jie

doi:10.1016/j.apenergy.2014.05.023

Cited by 190 publications

(81 citation statements)

References 24 publications

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“…By using the method introduced in [20], the forecasting results in one time slot are obtained, which are shown in Table 5. different values in spite of the same initial prices.…”

Section: Basic Datamentioning

confidence: 99%

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Liu

Jie

Wang

2017

J. Mod. Power Syst. Clean Energy

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From the perspective of transactive energy, the energy trading among interconnected microgrids (MGs) is promising to improve the economy and reliability of system operations. In this paper, a distributed energy management method for interconnected operations of combined heat and power (CHP)-based MGs with demand response (DR) is proposed. First, the system model of operational cost including CHP, DR, renewable distributed sources, and diesel generation is introduced, where the DR is modeled as a virtual generation unit. Second, the optimal scheduling model is decentralized as several distributed scheduling models in accordance with the number of associated MGs. Moreover, a distributed iterative algorithm based on subgradient with dynamic search direction is proposed. During the iterative process, the information exchange between neighboring MGs is limited to Lagrange multipliers and expected purchasing energy. Finally, numerical results are given for an interconnected MGs system consisting of three MGs, and the effectiveness of the proposed method is verified.

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“…By using the method introduced in [20], the forecasting results in one time slot are obtained, which are shown in Table 5. different values in spite of the same initial prices.…”

Section: Basic Datamentioning

confidence: 99%

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Liu

Jie

Wang

2017

J. Mod. Power Syst. Clean Energy

Self Cite

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“…The PV-MG is connected to bulk power system at the point of common coupling (PCC), the electricity power generated in PV-MG is preferentially supplied to meet the local loads, and exchange electricity with the bulk power system if in need. (1) PV resource: PV power generation is considered as the renewable energy generation resources most suitable to popularized and applied in the user side [30]. PV system is composed of PV array and PV DC/AC inverters.…”

Section: System Architecture Of Pv-based Microgrid (Pv-mg)mentioning

confidence: 99%

Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

et al. 2016

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Abstract:In order to solve the influence of uncertain photovoltaic power (PV) on the stable operation of microgrid (MG), demand response (DR) and battery energy storage system (BESS) need to be introduced simultaneously into the operation optimal scheduling of PV-based microgrid (PV-MG). Therefore, it is of great significance for commercial investment decisions of PV-MG to consider the influence of DR on BESS optimal sizing. Under the peak-valley time-of-use (TOU) price, this paper builds cross-time DR models based on price elasticity matrix. Furthermore, through the introduction of DR and BESS into PV-MG scheduling optimization, the MG investment and benefit model is proposed. Considering the constraint condition such as co-ordination of supply and demand, electricity price elasticity and energy loss of storage system, the improved non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimal allocation model of the BESS with the target of maximum PV consumptive rate and annual net profits. The optimization method was applied to a PV-MG in Guangdong. Through the regulation and control effect of demand response and BESS on load distribution, the uncertainties PV power can be suppressed so as to improve the PV system consumptive level, which is of great guiding significance for BESS optimal sizing under this situation.Keywords: time-of-use (TOU) price; multi-period demand response (DR); battery energy storage system (BESS); photovoltaic consumptive rate; investment and benefit model; multi-objective optimization; non-dominated sorting genetic algorithm II (NSGA-II) algorithm

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“…Also applied to wind forecasting, (Liu et al, 2015) proposes a new hybrid approach based on the Secondary Decomposition Algorithm (SDA) and the Elman neural networks. A hybrid load forecasting model with parameter optimization is proposed for short-term load forecasting in (Liu et al, 2014), being composed of Empirical Mode Decomposition (EMD), Extended Kalman Filter (EKF), Extreme Learning Machine with Kernel (KELM) and Particle Swarm Optimization (PSO).…”

Section: Solar Forecastmentioning

confidence: 99%

Data-Mining-based filtering to support Solar Forecasting Methodologies

Pinto

Marques

Sousa

et al. 2017

ADCAIJ

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A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids

Cited by 190 publications

References 24 publications

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

Data-Mining-based filtering to support Solar Forecasting Methodologies

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