This review paper provides a critical interpretation and analysis of almost 150 dedicated optimization research papers in the field of droop-controlled islanded microgrids. The significance of optimal microgrid allocation and operation studies comes from their importance for further deployment of renewable energy, reliable and stable autonomous operation on a larger scale, and the electrification of rural and isolated communities. Additionally, a comprehensive overview of islanded microgrids in terms of structure, type, and hierarchical control strategy was presented. Furthermore, a larger emphasis was given to the main optimization problems faced by droop-controlled islanded microgrids such as allocation, scheduling and dispatch, reconfiguration, control, and energy management systems. The main outcome of this review in relation to optimization problem components is the classification of objective functions, constraints, and decision variables into 10, 9 and 6 distinctive categories, respectively, taking into consideration the multi-criteria decision problems as well as the optimization with uncertainty problems in the classification criterion. Additionally, the optimization techniques used were investigated and identified as classical and artificial intelligence algorithms with the latter gaining popularity in recent years. Lastly, some future trends for research were put forward and explained based on the critical analysis of the selected papers.

In this paper a realistic medium voltage (MV) network with four different distributed generation technologies (diesel, gas, hydro and wind) along with their excitation and governor control systems is modelled and simulated. Moreover, an exponential model was used to represent the loads in the network. The dynamic and steady state behavior of the four distributed generation technologies was investigated during grid-connected operation and two transition modes to the islanding situation, planned and unplanned. This study aims to address the feasibility of planned islanding operation and to investigate the effect of unplanned islanding. The load sharing islanding method has been used for controlling the distributed generation units during grid-connected and islanding operation. The simulation results were validated through various case studies and have shown that properly planned islanding transition could provide support to critical loads at the event of utility outages. However, a reliable protection scheme would be required to mitigate the adverse effect of unplanned islanding as all unplanned sub-cases returned severe negative results.

In highly penetrated microgrids (MGs), the problem of voltage and frequency deviations exceeding their permissible limits, becomes significant with higher share of renewable based distributed generation (DG). The existing real-time control systems of highly penetrated MGs cannot cope on its own with such deviations, owing to the large generation and demand mismatch mainly during off-peak hours. A dump load (DL) can help with voltage and frequency regulation by consuming excess generation. However, further investigation is required to highlight the importance of optimal DL allocation on the operation of MGs. The mixed-integer distributed ant colony optimization is introduced as a novel application in droop controlled islanded MGs to minimize voltage and frequency deviations and system losses. The optimization problem was formulated as a single- and multiobjective problem to allocate a DL and the optimal droop settings for DG in islanded microgrid (MG) during off-peak hours. The proposed optimization method was teamed up with a special backward/forward sweep load flow method to account for DG droop characteristics and enhance the solution convergence. The method was applied to the IEEE 69- and 118-test systems and validated against competitive swarm and evolutionary metaheuristics. Results have shown that an optimally sized and allocated DL with optimized droop setting could minimize voltage and frequency deviations to an acceptable level, while reducing power losses incurred by the installation of such load into the MG.

Dump load (DL) utilization at low demand hours in highly penetrated islanded microgrid is of great importance to offer voltage and frequency regulation. Additionally, load flow (LF) convergence is vital to optimize the working states of the DL allocation problem. Hence, more analysis is necessary to highlight the significance of DL in power regulation while observing the influence of LF on solution accuracy. This article proposes two LF techniques derived from backward/forward sweep (BFS), viz., general BFS (GBFS) and improved special BFS (SBFS-II). The latter is based on global voltage shared between generating units, while the former has a more general approach by considering generating bus’s local voltage. The optimal sizing and sitting of DL with optimum droop sets are determined using the mixed-integer distributed ant colony optimization (MIDACO) with the two new LF methods. The optimization problem was formulated to minimize voltage and frequency deviations as well as power losses. The problem was validated on IEEE 69- and 118-bus systems and compared with established metaheuristics. Results show that DL allocation using MIDACO with SBFS-II and GBFS has improved the solution speed and accuracy, respectively. Furthermore, the enhanced voltage and frequency results highlight DL as an efficient power management solution.

Reliable droop-controlled islanded microgrids are necessary to expand coverage and maximize renewables potential. Nonetheless, due to uncertainties surrounding renewable generation and load forecast, substantial power mismatch is expected at off-peak hours. Existing energy management systems such as storage and demand response are not equipped to handle a large power mismatch. Hence, utilizing dump loads to consume excess power is a promising solution to keep frequency and voltage within permissible limits during low-load hours. Considering the uncertainty in wind generation and demand forecast during off-peak hours, the dump load allocation problem was modeled within a scenario-based stochastic framework. The multi-objective optimization with uncertainty was formulated to minimize total microgrid cost, maximum voltage error, frequency deviation, and total energy loss. The mixed-integer distributed ant colony optimization was utilized in a massive parallelization framework for the first time in microgrids to solve the decomposed deterministic problem of the most probable scenarios. Moreover, a flexible and robust load-flow method called general backward/forward sweep was used to obtain the load-flow solution. The optimization problem was applied to the IEEE 69-bus and 118-bus systems. Furthermore, a cost benefit analysis was provided to highlight the proposed method’s advantage over battery-based power management solutions. Lastly, the obtained results further demonstrate the fundamental role of dump load as power management solution while minimizing costs and energy losses.

Dump load (DL) allocation in droop-controlled islanded microgrid (DCIMG) is vital to consume excess generation at off-peak hours and provide voltage and frequency (V-f) support. Furthermore, convergence of load flow (LF) solution is necessary to determine optimal working points of any DCIMG. Proposed in this paper, two LF methods based on the famous backward/forward sweep (BFS): improved special BFS (SBFS-II) and general BFS (GBFS). The former method is based on global voltage variable spread among all distributed generation (DG), while the latter is more general by considering local voltage measurement at each DG. The multi-objective problem of DL allocation in highly penetrated DCIMG to minimize V-f deviations and power losses was investigated using the two LF methods combined with mixed-integer distributed ant colony optimization (MIDACO). The problem was applied to the IEEE-33 bus system, while solutions were subjected to various convergence tests. Results show SBFS-II and GBFS efficacy in calculation time and accuracy of the solution, respectively, for DL allocation problem in DCIMG.