A Microgrid Energy Management System Based on the Rolling Horizon Strategy

Palma‐Behnke, Rodrigo; Benavides, Carlos; Lanas, Fernando; Severino, Bernardo; Reyes, Lorenzo; Llanos, Jacqueline; Sáez, Doris

doi:10.1109/tsg.2012.2231440

Cited by 649 publications

(316 citation statements)

References 24 publications

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“…In (Palma-behnke et al, 2013), authors employed an online approach called rolling horizon strategy to schedule energy storage devices and solve UC issues by using mixed integer programming optimization, based on two-day-ahead power forecasting results. Additionally, demand management mechanism was integrated to shift consumer's behaviour and maximize renewable energy utilization.…”

Section: Centralized Control Scheme and Optimizationmentioning

confidence: 99%

A review of forecasting algorithms and energy management strategies for microgrids

2018

Systems Science & Control Engineering

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As an autonomous subsystem integrating with the utility, a microgrid system consists of distributed energy sources, power conversion circuits, storage units and adjustable loads. With the high penetration of distributed generators, it is challenging to provide a reliable, consistent power supply for local customers, because of the time-varying weather conditions and intermittent energy outputs in nature. Likewise, the electricity consumption changes due to the season effect and human behaviour in response to the changes in electricity tariff. Therefore, studies on accurate forecasting power generation and load demand are worthwhile in order to solve unit commitment and schedule the operation of energy storage devices. The paper firstly gives a brief introduction about microgrid and reviews forecasting algorithms for power supply side and load demand. Then, the mainstream energy management approaches applied to the microgrid, including centralized control, decentralized control and distributed control schemes are presented. A number of the optimal energy management algorithms are highlighted for centralized controllers based on short-term forecasting information and a generalized centralized control scheme is thus summarized. Consensus protocol is discussed in this paper to solve the cooperative problem under the multi-agent system-based distributed energy system. Finally, the future of energy forecasting approaches and energy management strategies are discussed. ARTICLE HISTORY

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Section: Centralized Control Scheme and Optimizationmentioning

confidence: 99%

A review of forecasting algorithms and energy management strategies for microgrids

2018

Systems Science & Control Engineering

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“…In this paper, we assume the day-ahead contracts have already been determined and restrict our study on the real-time scheduling. RHO has been proved to be an effective approach for real-time dispatch [27]. By continuously proceeding over the optimization horizon, updating the system states and repetitively performing the optimization over the rolling future horizon, the RHO can effectively mitigate the negative impacts of the forecast errors.…”

Section: Rolling Horizon Optimization Based Real-time Operation Modelmentioning

confidence: 99%

Rolling horizon optimization for real-time operation of thermostatically controlled load aggregator

Luo

Ranzi

Dong

2017

J. Mod. Power Syst. Clean Energy

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Thermostatically controlled loads (TCLs) have great potentials to participate in the demand response programs due to their flexibility in storing thermal energy. The two-way communication infrastructure of smart grids provides opportunities for the smart buildings/houses equipped with TCLs to be aggregated in their participation in the electricity markets. This paper focuses on the realtime scheduling of TCL aggregators in the power market using the structure of the Nordic electricity markets a case study. An International Organization of Standardization (ISO) thermal comfort model is employed to well control the occupants' thermal comfort, while a rolling horizon optimization (RHO) strategy is proposed for the TCL aggregator to maximize its profit in the regulation market and to mitigate the impacts of system uncertainties. The simulations are performed by means of a metaheuristic optimization algorithm, i.e., natural aggregation algorithm (NAA). A series of simulations are conducted to validate the effectiveness of proposed method.

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“…BESS have been mostly employed in islanded microgrids to store excess renewable generation, see e.g., [4]. However, due to their decreasing cost, they are becoming of increasing interest in grid-connected systems to perform dispatchability, peak shaving, voltage control, and self-consumption of local distributed generation, see e.g [5]- [11].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the provision of ancillary services from storage systems using smart transformer and smart meters

Sossan

Christakou

Paolone

et al. 2017

2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)

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Abstract-The Smart Transformer, a solid-state transformer with control and communication functionalities, can be the ideal solution for integrating storage into the grid. By leveraging the knowledge of the grid state of distribution grids thanks to smart meters and/or dedicated remote terminal units, in the paper, it is proposed a control strategy for a MV/LV smart transformer (ST) with integrated storage to achieve: i) dispatched-by-design operation of the LV network by controlling the ST active power set-point on the MV power converter, and ii) voltage regulation of both MV and LV networks by controlling the reactive power injections of both LV and MV converter. The former is achieved by dispatching the active power flow of the LV network according to a profile established the day before the operation, called dispatch plan, with the objective of reducing the amount of regulating power required to operate the grid. It is based on the use of forecast to compute a dispatch plan, and a tracking problem to compensate in real-time the mismatch between realization and dispatch plan by taking advantage of the storage capacity. The latter is achieved by using sensitivity coefficients, which are calculated from the state of the grid and integrating the information on the network topology. The problem formulation is given in the paper, and the proof-ofconcept is shown by simulation using the IEEE 34 nodes test feeder and the CIGRE Low Voltage reference network.

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A Microgrid Energy Management System Based on the Rolling Horizon Strategy

Cited by 649 publications

References 24 publications

A review of forecasting algorithms and energy management strategies for microgrids

A review of forecasting algorithms and energy management strategies for microgrids

Rolling horizon optimization for real-time operation of thermostatically controlled load aggregator

Enhancing the provision of ancillary services from storage systems using smart transformer and smart meters

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