Improved time representation model for the simultaneous energy supply and demand management in microgrids

Abstract

This paper analyses the operational decision making procedures required to address the simultaneous management of energy supplies and requests in a microgrid scenario, in order to best accommodate arbitrary energy availability profiles resulting from an intensive use of renewable energy sources, and to extensively exploit the eventual flexibility of the energy requirements to be fulfilled. The optimization of the resulting short term scheduling problem in deterministic scenarios is addressed through a Mixed-Integer Linear Programming (MILP) mathematical model, which includes a new hybrid time formulation developed to take profit of the advantages of the procedures based on discrete time representations, while maintaining the ability to identify solutions requiring a continuous time representation, which might be qualitatively different to the ones constrained to consider a fixed time grid for decision-making. The performance of this new time representation has been studied, taking into account the granularity of the model and analyzing the associated trade-offs in front of other alternatives. The promising results obtained with this new formulation encourage further research regarding the development of decision-making tools for the enhanced operation of microgrids. Storage energy cost [m.u./kWh] Span of the time interval [h], Duration of consumption [h], Minimum power supply of source at interval [kW], Maximum power supply of source at interval [kW], Energy price to be sold to power grid at interval [m.u./kWh] , Minimum electricity storage of system at interval [kWh] , Maximum electricity storage of system at interval [kWh] , Initial storage level of system at interval [kWh] , , Period of time in which consumption is active at interval [h], Maximum initial time of consumption [h], Target initial time of consumption [h]  Binary variable indicating if consumption is active at interval t

A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids

“…Along this line, a discrete-time MILP formulation for the integrated management of energy production and demand was developed by Silvente et al [23]. More recently, Silvente et al [24] have presented a comparison between discrete-time and hybrid-time MILP formulations related to a microgrid under profit maximization. Also, a new MILP hybrid approach was presented by Silvente et al [25] with the aim of minimizing the operational cost of the overall system, and very recently Marietta et al [26], Silvente et al [27] have presented a discrete-time MILP formulation with flexible time windows for the allocation of energy loads into a rolling horizon framework.…”

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

“…Hence, a discrete-time MILP mathematical model was presented by Silvente et al (2012 and for the coordinated management of energy generation, storage and demand in a microgrid. Also, Silvente et al (2015a) presented a comparison between discrete and hybrid-time MILP formulations to maximise the profit of a microgrid. More recently, Silvente and Papageorgiou (2017) presented an MILP formulation to manage the generation, storage and demand of energy and heat within a microgrid.…”

An MILP formulation for the optimal management of microgrids with task interruptions

“…However, this may involve more computational time to reach the solution. The effect of the duration of the time interval in a discrete time formulation was studied by Silvente et al (2015). Other alternatives are to use hybrid and continuous time formulations, at the expense of an increase in the complexity of the mathematical model, increasing the computation effort.…”

“…Mohsenian-Rad and León-García (2010) presented a residential energy task scheduling formulation considering energy pricing models for smart houses. Silvente et al (2015) presented an MILP formulation for the coordinated management of generation and demand in a microgrid, taking into account renewable energy sources and the interconnection to the power grid. Penalty costs were applied in case of delays from the nominal target in the initial starting time of different energy tasks.…”