Optimal siting and sizing of tri-generation equipment for developing an autonomous community microgrid considering uncertainties

Hussain, Akhtar; Arif, Syed Muhammad; Aslam, M.; Shah, Syed Danial Ali

doi:10.1016/j.scs.2017.04.004

Cited by 49 publications

(17 citation statements)

References 40 publications

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“…Networking of several microgrids to form a microgrid network is considered as an advanced form and application of the single microgrid concept [34,35]. Therefore, recently studies have been conducted for transforming existing radial distribution networks to autonomous networked microgrids [36][37][38][39][40]. However, due to the autonomous operation of networked microgrids, consideration of system resiliency is necessary to cope up with major disturbance events.…”

Section: Literature Reviewmentioning

confidence: 99%

Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks

2019

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A power distribution network is a critical infrastructure in any society and any disruption has an enormous impact on the economy and daily lives. Therefore, the objective of this study is to transform the conventional power distribution systems into resilient autonomous microgrid networks by optimally sizing and siting the distributed generators (DGs). First, N main DGs are placed to transform an existing network into an autonomous microgrid network. Second, all the possible combinations of the initially deployed DGs are made and then the outage of 1 to N − 1 DGs is considered. Considering the outage of DGs in each combination (one at a time), the resiliency of the network is analysed. Amount of load shedding, total power loss in the network, and voltage limits are analysed in this step. Finally, based on the resiliency analysis, additional DGs are placed to enhance the resiliency of the transformed network. Heuristic methods (particle swarm optimisation and genetic algorithm) are used for both sizing and siting of DGs during the first and the second steps. The objective of the formulation is to minimise load shedding, total power loss (active and reactive), and voltage deviations in the network during DG outages. ω i weight function for velocity of particle i ω max , ω min min. and max. weights for velocity of particles k max , k maximum and current iteration numbers

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Section: Literature Reviewmentioning

confidence: 99%

Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks

2019

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“…Various energy management strategies have been used in the literature for scheduling microgrids/multi-microgrids [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Among those, the cooperative multi-microgrid community has gained popularity due to its merits like minimum operation cost of the entire network, network level resource-aware optimization, better utilization of efficient units of individual microgrids, etc.…”

Section: System Configurationmentioning

confidence: 99%

Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations

et al. 2017

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Abstract:In this paper, an optimal energy management strategy for a cooperative multi-microgrid system with combined cooling, heat and power (CCHP) is proposed and has been verified for a test case of building microgrids (BMGs). Three different demand types of buildings are considered and the BMGs are assumed to be equipped with their own combined heat and power (CHP) generators. In addition, the BMGs are also connected to an external energy network (EEN), which contains a large CHP, an adsorption chiller (ADC), a thermal storage tank, and an electric heat pump (EHP). By trading the excess electricity and heat energy with the utility grid and EEN, each BMG can fulfill its energy demands. Seasonal energy demand variations have been evaluated by selecting a representative day for the two extreme seasons (summer and winter) of the year, among the real profiles of year-round data on electricity, heating, and cooling usage of all the three selected buildings. Especially, the thermal energy management aspect is emphasized where, bi-lateral heat trading between the energy supplier and the consumers, so-called energy prosumer concept, has been realized. An optimization model based on mixed integer linear programming has been developed for minimizing the daily operation cost of the EEN while fulfilling the energy demands of the BMGs. Simulation results have demonstrated the effectiveness of the proposed strategy.

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“…The SOC limits of BESS unit in m th microgrid are given by Equation (15). Equation 16shows that SOC at any time interval t depends upon the amount of electricity charged/discharged at that time interval and the SOC of the previous time step.…”

Section: Battery Constraintsmentioning

confidence: 99%

“…In addition, the computational burden is reduced in robust optimization due to consideration of uncertainty bounds only. Due to the abovementioned merits, various forms of robust optimization have been used for the optimal operation of microgrids [8,9,13,15].…”

Section: Introductionmentioning

confidence: 99%

Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids

2017

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Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations. Similarly, the participation of enrolled customers in DR programs is also uncertain and, among various other factors, uncertainty in market prices is a major cause. Therefore, in this paper, the impact of DR program intensity and BESS size on the operation of networked microgrids is analyzed while considering the prevailing uncertainties. The uncertainties associated with forecast load values, output of renewable generators, and market price are realized via the robust optimization method. Robust optimization has the capability to provide immunity against the worst-case scenario, provided the uncertainties lie within the specified bounds. The worst-case scenario of the prevailing uncertainties is considered for evaluating the feasibility of the proposed method. The two representative categories of DR programs, i.e., price-based and incentive-based DR programs are considered. The impact of change in DR intensity and BESS size on operation cost of the microgrid network, external power trading, internal power transfer, load profile of the network, and state-of-charge (SOC) of battery energy storage system (BESS) units is analyzed. Simulation results are analyzed to determine the integration of favorable DR program and/or BESS units for different microgrid networks with diverse objectives.

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Optimal siting and sizing of tri-generation equipment for developing an autonomous community microgrid considering uncertainties

Cited by 49 publications

References 40 publications

Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks

Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks

Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations

Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids

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