Multi‐objective operation of distributed generations and thermal blocks in microgrids based on energy management system

Abstract

The optimal multi-objective operation of microgrids with distributed generations and thermal block based on the energy management system is presented. In the thermal block, the combined heat and power system and boiler and thermal storage system supply the load of the block thermal. Therefore, the proposed strategy minimises three objectives of an microgrid's operation, that is, cost, energy loss and voltage deviation functions. Also, it is subject to AC power flow equations, system operation limits and distributed generations and thermal block constraints. The problem is modelled as a multi-objective optimisation method; then is converted to a single-objective problem model using the ε-constraintbased Pareto optimisation. Moreover, the proposed strategy includes uncertainties of the load, energy price and active power of renewable distributed generations; hence, a stochastic programming based on the hybrid Mont Carlo simulation and point estimate method is used to model these uncertain parameters. Then, the teaching-learning-based optimisation and firefly algorithm are utilised to solve the problem to achieve a reliable optimal solution. Finally, the proposed strategy is simulated on the IEEE 69-bus microgrid, and thus, the capabilities of the proposed strategy are extracted.Nomenclature: π, Probability of occurrence of a scenario; η b , Efficiency of the boiler; ρ ele , ρ gas , ρ heat , Electricity, gas and heat energy price ($ MWh −1 ); η mt , Efficiency of the MT; η st , Efficiency of the TSS; η t , η l , η h , Electricity, loss and heat efficiency of the CHP; A L , The bus incidence matrix considering the line current direction; C MT , Operation price of the MT ($ MWh −1 ); E, Stored energy in the TSS in perunit (p.u.); E min , E max , Minimum and maximum allowed stored energy in the TSS (p.u.); g, b, Conductance and susceptance of a line (p.u.); H BO , G BO , Heat and gas power of the boiler (p.u.); H BOmin , H BOmax , Minimum and maximum allowed heat power of the boiler (p.u.); H ch , H dch , Heat charging and discharging power of the TSS (p.u.); H CHP , G CHP , Heat and gas power of the CHP (p.u.); H CHPmin , H CHPmax , Minimum and maximum allowed heat power of the CHP (p.u.); H D , Heat demand (p.u.); H STmax , Maximum heat charging/discharging power of the TSS (p.u.); n, l, N, Bus and bus, and set of buses; P CHPmin , P CHPmax , Minimum and maximum allowed active power of the CHP (p.u.); P D , Q D , Active and reactive demand (p.u.); P G , Q G , Active and reactive power of the distribution station (p.u.); P L , Q L , Active and reactive power flow on the line (p.u.); P MT , P CHP , Active power of micro-turbine (MT) and CHP (p.u.); P MTmin , P MTmax , Minimum and maximum allowed active power of the MT (p.u.); P PV , P WT , Active power of photovoltaic (PV) and wind turbine (WT) (p.u.); P PVmax , Maximum generation active power of the PV (p.u.); P WTmax , Maximum generation active power of the WT (p.u.); ref, Slack bus; S Gmax , Maximum capacity of the distribution station (p.u.); S Lmax , Maximum capac...

Participation of Grid-Connected Energy Hubs and Energy Distribution Companies in the Day-Ahead Energy Wholesale and Retail Markets Constrained to Network Operation Indices

“…(ii) e electrical part of the EH has low flexibility in the presence of RES [1]. Also, its heating part will have low flexibility in the presence of CHPs [23]. Low flexibility causes the results of real-time and dayahead operations to be the same, which will lead to an imbalance of demand and supply for EHs in realtime operation [24].…”

Harnessing power system flexibility under multiple uncertainties

“…Moreover, the proposed model has been defined in transmission level in which the ratio of X to R is more significant than in distribution networks. As a result, using DC power flow can be more efficient at the transmission level with lower computational complexity, while the linearized AC power flow should be applied to measure system flexibility at the distribution level [43][44][45].…”

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A Review on Thermal Energy Modelling for Optimal Microgrids Management

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