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This paper proposes a stochastic optimal power allocation strategy for a droop controlled islanded microgrid (DCIMG) with a medium X/R ratio in presence of combined heat and power-based distributed generators (DGs), renewable generators, switched capacitor banks, plug-in hybrid electric vehicle (PHEV) loads, voltage dependent electric loads and heat loads. Optimal values of stochastic power demand due to PHEV charging and discharging load are determined based on the goals of achieving target state-of-charge of PHEV batteries and effective peak shaving during peak load periods, respectively. Active power of dispatchable DGs is optimally allocated depending on the simultaneous fulfillment of an economic objective, an environmental objective along with three network related objectives. Hong's 2m+1 point estimate method (PEM) and Hong's 2m+1 PEM coupled with Nataf transformation (NT) are employed to handle the uncorrelated uncertainties accompanied with PHEV load demand and the correlated uncertainties associated with spatially correlated renewable generation and load demand. Active and reactive power static droop coefficients, and frequency and voltage reference settings of the dispatchable DG units are considered as the control variables for optimal dispatching of active power. A modified hybrid particle swarm and grey wolf optimizer embedded in fuzzy framework is used to solve the constrained multi-objective optimization problem. The efficacy of the proposed framework is validated on a 33-node droop controlled islanded microgrid test system. • A modified hybrid PSO-GWO algorithm is proposed for optimal power allocation among dispatchable DGs. • Fuzzy based approach is adopted for solving multi-objective optimization problem. • PHEV charging/discharging load and voltage dependent load models are used. • Hong's 2 m + 1 PEM with Nataf Transformation is employed to model the correlated uncertainties. [ABSTRACT FROM AUTHOR]

Optimal operation in islanded microgrids is one of most fundamental and prominent non-linear optimization problems investigated with new/different objective(s) with respect to the adaptable system constraints concerning to effective power dispatch. This paper presents a novel optimization method with distinct goals for optimal dispatch of both real and reactive powers from Distributed Generations (DGs) in islanded microgrids. The proposed method implements it by optimal tuning of droop parameters in dispatchable DGs, towards optimal dispatch strategy of each DG in the microgrid. The objectives considered are generation cost, emission, and diversity in voltage profile from unity. A new variant in Multi-Objective Particle Swarm Optimization (MOPSO) with Non-Linear Time Variation (NLTV) feature collectively referred to as NLTV-MOPSO has been proposed to solve the formulated problem. The uniqueness in proposed MOPSO are NLTV and modified mutation strategy in exploiting entire search space; there from a single optimal solution is obtained from Pareto by fuzzification. The effectiveness of proposed method with/without solar PV along with energy storage is verified with other MOPSO based algorithms. Also the simulation results for plug-and-play capability were tested effectively in proposed method. The results show the adaptability and performance of the proposed method for considered multi-objective optimal power flow problem in islanded microgrid. [ABSTRACT FROM AUTHOR]

Power system restructuring is a vital constituent in the modern power scenario. Existing research works presented renewable power equipped restructured power systems with conventional power plants. Whereas, at present, experiments on restructured microgrids are very limited. Moreover, inter-area power sharing is evaluated by considering only the effect of the line inductance. However, the line resistance has a significant role in power sharing for a microgrid system where power distribution takes place within small power networks. Therefore, this paper presents a restructured two-area interconnected microgrid scenario with an improved tie-line power model to perform automatic generation control (AGC) operation. First, the effect of the improved tie-line model is implemented under a two-area interconnected conventional microgrid system. Furthermore, the proposed tie-line power model is tested under restructured microgrid scenario considering various loading conditions. A novel proportional-derivative feedbacked proportional-integral-derivative (PDFPID) controller is adopted for secondary frequency control application. Mathematical expressions are formulated and proposed to improve the dynamics of the suggested power system model. Furthermore, the results obtained from the proposed mathematical equations are validated with the help of simulation results. Finally, the feasibility of the proposed microgrid model is tested in a 12-node radial distributed network. [ABSTRACT FROM AUTHOR]

This paper presents a heuristic approach to address the restoration of droop-controlled islanded microgrids (DCIMGs) in the event of a sectionalising switch (SS) failure. The proposed approach consists of two-step procedures to ensure the smooth operation of the network. First, a faulty SS is isolated by replacing it with a tie switch (TS) that acts as an SS. Then, the switching configuration is adjusted to improve the performance of DCIMGs. To assess the efficacy of the proposed approach, the 33-bus and 118-bus DCIMG test networks are considered. For fulfilling the load demand in each DCIMG, droop-controlled DGs (DDGs) and renewable DGs (RDGs) are employed. Along with satisfying load demand, DGs decrease the power loss and voltage drop. Additionally, shunt capacitor banks (SCBs) are also incorporated into the DCIMG to improve the voltage profile. In conjunction with the load flow analysis, the power injection of the DDGs are determined using the modified Newton–Raphson method. For determining the power injection of RDGs and the size of SCBs, particle swarm optimisation method is implemented. The paper investigates the effectiveness of the proposed method for resolving SS faults in DCIMGs over a 24 hour period amidst hourly demand and renewable power generation variation. By employing Hong's 2 m + 1 point estimate method, the uncertainties associated with load demands and renewable power generation are also considered. The effectiveness of the issue formulation is verified using M a t L a b R 2023 b. With the proposed method, the voltage and frequency can be effectively maintained after the network is restored following a fault. • Automatic restoration approach reduces customer disturbances. • The proposed heuristic approach aims to restore the operation of island microgrids post switching failure. • Hourly DG power injection depends on load demand and renewable power generation uncertainties. • Changing switching configuration reduces network losses and voltage drop. [ABSTRACT FROM AUTHOR]

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations' sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong's (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively. [Display omitted] • Priority-ordered incentive-based DR program for the EV and non-EV loads is proposed. • Fast dynamic reconfiguration of the off-grid microgrid is presented. • Multi-objective energy scheduling problem for the off-grid microgrid is modeled. • Utopia-tracking approach is used to solve the conflicting multi-objective problem. [ABSTRACT FROM AUTHOR]

Summary: The traditional droop control method in a DC microgrid (DCMG) fails to achieve accurate current sharing among parallel‐connected dispatchable units when the connecting feeders have unequal resistances, or when the units supply local loads apart from loads at the common dc bus. In this paper, a novel modification of the droop control strategy (additional feedback term in the outer control loop) is suggested to overcome the above limitations. The proposed method is robust, noise‐resilient, amenable to the "plug and play model", and employs local measurements, thereby, dispensing with the need for communication between remote nodes. A small‐signal model of the system with the proposed control strategy is derived for carrying out eigenvalue and participation factor analysis. A sensitivity analysis is also carried out to inspect the impact of variation of system parameters on small‐signal stability. The effect of time delay and noise on system small‐signal stability is also studied. A robust stability analysis is also carried out. The proposed control strategy is easily implementable on digital hardware. Time domain simulations using PSCAD (Power System Computer Aided Design)/EMTDC(Electromagnetic Transients including DC) is carried out to validate and compare the performance of the proposed technique vis‐a‐vis other existing methods. Performance of the proposed method is good even with time delay and noise. [ABSTRACT FROM AUTHOR]

In this study, the authors present a linear power flow method for droop-controlled DC microgrids (MGs). Traditional power flow techniques do not apply to droop-controlled DCMGs owing to the power and voltage flexibility capability of the droop-controlled dispatchable generation units. Therefore, new power flow techniques embodying the droop-control operation philosophy were developed by the researchers. The existing methods are time-consuming since they involve iterative solutions of a set of non-linear equations. The proposed linear power flow method is accurate, simple, and gives faster results in comparison to the existing methods. The power flow model applies to all load types (constant power, constant current, and constant resistance) and works for both radial and non-radial system configurations. The linear power flow can be applied for convex optimisation, optimal power flow, and network reconfiguration problems. The proposed method is validated on five test systems. A comparison with a modified Newton--Raphson power flow demonstrates that the proposed linear power flow technique provides accurate results. The maximum error in voltage calculation is not more than 0.0812%. The performance of the proposed method is also acceptable for heavy loads and high line resistances. The computational time reduces significantly with the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a method to determine optimal droop values of dispatchable distributed generation (DG) units in an islanded droop-controlled dc microgrid. Objectives are to minimize fuel cost and maximize small signal stability of the system. In this paper, a detailed state-space model of an islanded droop-controlled dc microgrid including the converter, network parameters, and network interconnection is developed. The developed model is then used to obtain the dominant eigen value of the system. Particle swarm optimization is used to determine the optimal droop values of the dispatchable DG units in order to simultaneously reduce the fuel cost, increase system damping, and push the most dominant eigen value away from the imaginary axis to the left of the s-plane. The bi-objective optimization is solved using a fuzzy max–min based approach. The proposed method is validated on a sample three-bus droop-controlled DCMG test system. Comparison of the result obtained by the fuzzy max–min approach with that of controlled elitist genetic algorithm is also presented in this paper. [ABSTRACT FROM AUTHOR]

In this paper, economic load dispatch (ELD) problem of a microgrid is formulated considering load and renewable generation uncertainties. Both load and generation uncertainties are modeled using triangular fuzzy numbers. The ELD problem minimizes the fuel cost of the dispatchable distributed generators (DGs) in the microgrid. The reserve requirement for generation and load uncertainties is formulated. Next, the reserve requirement for a stable transition of a grid-connected microgrid to islanded mode under load and generation uncertainties is also formulated. Moreover, the reserve requirement for a stable transition of an islanded microgrid to grid-connected mode under load and generation uncertainties is also formulated in this paper. The effects of these constraints on the daily fuel cost of dispatchable DGs are then investigated. A three-area microgrid test system is considered for the simulation. Each area in the microgrid comprises dispatchable DGs, loads, wind power DGs and solar power DGs. The economic dispatch problem is solved using four different techniques: (i) bisection method, (ii) regula falsi method, (iii) golden section method, and (iv) teaching-learning-based optimization (TLBO) technique. Simulation is carried out considering 24 h load, wind generation and solar generation profiles. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the effect of integration of combined heat and power (CHP) units, renewables, shunt capacitors, and a heat boiler in a grid connected microgrid (GMG) system. In addition to that, a comparative study is conducted between the GMG and zero bus concept based microgrid (ZBMG) systems. In ZBMG, a predefined amount of power is injected from the grid. Therefore, one of the dispatchable distributed energy resources (DERs) in the ZBMG maintains the power balance and the corresponding DER unit is entitled as zero bus DER. In this paper, eight different microgrid configurations are formed by several combinations of renewable and non renewable sources, which are categorized under GMG and ZBMG systems. The multiple objectives such as total cost, emission, and energy loss are combined and solved using fuzzy technique and particle swarm optimization (PSO) algorithm. Also, point estimate method (PEM) is implemented to handle the uncertainty in renewables. The effect of grid, dispatchable units, renewables, and heat boiler on the objectives in each microgrid configuration is inspected. The results show that the microgrid configuration with non renewables is attained minimum cost and has a minimum emission with renewables. The integration of all types of sources in a microgrid configuration leads to a decrement in all the objectives. The proposed method is tested on a 33-bus grid connected microgrid system considering electrical and heat loads. • Analysis of microgrid system with different combinations of CHP units and renewables. • Study the impact of optimal allocation of sources in each combination on objectives. • Microgrid system with predefined power from the grid shows satisfactory results. [ABSTRACT FROM AUTHOR]

Recently researchers have focused on the optimal operation of droop-controlled islanded microgrids (DCIMG). However, most of the papers consider only the economic objective. But, with increasing focus on environmental compliance, it has also become necessary to reduce emission from generation activities. Furthermore, most of the previous papers do not consider the heat demand in the microgrid and the uncertainties involved in load and renewable generation forecasting. This paper bridges all these gap areas by presenting a method for determining optimal droop settings of dispatchable distributed generation units in a DCIMG. The objectives are to minimize the operational cost and minimize the emission in the islanded droop-controlled microgrid while meeting all the operational constraints. The proposed formulation takes into account the electricity demand, the heat demand, load uncertainties, and renewable power uncertainties in the microgrid. Load and renewable power uncertainties are modeled by a stochastic scenario based approach. The multiobjective optimization problem is solved using fuzzified particle swarm optimization. The proposed method is validated on a 33-bus DCIMG test system. The results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a distributed local control scheme for dc microgrid is proposed along with the basic droop control. It eliminates the limitations of droop control when the distributed generators are geographically distributed, for which, the line resistances cannot be neglected. Effects of line inductance and constant power loading (CPL) are investigated by analyzing the voltage tracking transfer function for single source system. Stability of two sources single load microgrid with proposed controller is investigated. Simulated responses are presented for two sources single load microgrid (for the sake of simplicity) to depict the proper load sharing and voltage improvement capability of the proposed control method with the consideration of line resistances. However, this can be extended to multiple-source multiple-load configuration connected to the dc bus. A comparison of the result is presented to show the better performance of the proposed control scheme as compared to the conventional droop control and hierarchical secondary control. The interconnected operation of the microgrid is also investigated to show the applicability of the proposed control in the interconnected mode. A centralized controller in each area is used to make the tie-line power flow zero at steady state. [ABSTRACT FROM PUBLISHER]

The growth in the energy demand of the microgrid due to the inclusion of electric vehicles (EV) and other non-EV loads introduces several challenges for the operators in scheduling energy for the microgrid. The inclusion of demand response (DR) program in the operational planning of microgrid can decrease the burden on the operator, but it requires aggregators for the efficient coordination between the operator and several potential DR participants of the microgrid. In this work, an optimization model is proposed to include a novel incentive-based DR program in the energy management problem of the reconfigured grid-connected microgrid. Two different aggregators for EV and non-EV loads are included in the work as an interface between the operator and DR participants. The objective of the proposed DR program is to maximize the incentives offered to the DR participants while maintaining uniformity in terms of rewards and distress delivered to the DR participants. The proposed work is analyzed on a static model of a 33-bus grid-connected microgrid consisting of EV charging stations, renewable energy sources, and diesel generators at different locations. The microgrid is reconfigured at each operating interval to minimize the power lost in the network. The result confirms that optimality is achieved at the source, distribution, and load side of the microgrid. For a day-ahead operation, it has been found that the energy dependency of a microgrid on the utility grid and conventional energy source is reduced by 9.62% and 29.06%, respectively. [Display omitted] • Optimal coordination of aggregators in the reconfigured grid-connected microgrid. • Proposal of novel method to reward the participants of demand response program. • Energy management issue due to pre-fixed power exchange between grid and microgrid. • Application of improved JAYA algorithm in the energy management issue of microgrid. • Analysis of uncertainties in DR participants using Hong's point estimation method. [ABSTRACT FROM AUTHOR]

Increased investiture towards Distribution Generation (DG) in recent past had prompted for transmutation of existing networks to form microgrids that can operate autonomously or in conjunction with main-grid. In particular, the augmenting zeal for renewable DGs is thriving at recent times; but non-dispatchability and uncertain nature pose specific challenges, which is the primary concern in this work. This paper employs Point Estimate Method (PEM) for handling uncertainties to solve the probabilistic-optimal power flow problem (POPF) with multiple objectives formulated in an islanded microgrid with droop coordinated DGs including uncertainties involved in load, wind and solar PV with suitable probability distributions. The devised POPF problem is proposed to solve for optimal droop parameters by multi-objective ant-lion optimization algorithm; tested and verified on modified 33-bus system. Furthermore, in actuality, spatial correlations among renewables play vital role in devising operational schedule for energy management strategies. This paper deals with PEM compounded with Nataf Transformation for POPF to handle spatial correlations in renewable generations. The dominance of wind correlation over solar PV correlations in POPF problem is highlighted. The robustness of proposed approach is verified with benchmark Monte Carlo Simulation (MCS) to affirm about its accuracy for suitable replacement of proposed approach to MCS. [ABSTRACT FROM AUTHOR]

Multi-objective expansion planning of the 33-bus droop-regulated islanded microgrid (DRIMG) by incorporating the impact of hourly probabilistic modelling of generated renewable power and consumer demand with respect to every season is presented in this work. The energy and power ratings of the BESS, power ratings of the renewable DG units, and the hourly active and reactive power droop coefficients of the dispatchable DG units are simultaneously optimized using weighted-aggregated grey wolf optimizer (GWO). The objectives considered are the minimization of the annual operation and maintenance (O&M) cost, annual emission and annual energy loss, and the maximization of the annualized benefit due to deferred network upgrades. A maximum reduction of 28%, 32%, and 26% is observed in the yearly O&M expenses, emission, and energy loss, respectively, by applying the proposed method on four scenarios. It is inferred that hourly droop optimization together with the optimal sizing of a PV-wind-BESS system enables the deferral of the relevant upgrades by 19 years, accompanied by an associated yearly monetary benefit of $ 1,344,107. An analysis of the errors pertaining to 95% and 98% confidence intervals, along with a performance comparison with other methods, based on the objectives considered, is also incorporated. • Multi-objective planning of an islanded microgrid is done with grey wolf optimizer. • Renewable DG and BESS sizes, and dispatchable DG droop coefficients are optimized. • The deferral of upgrades by 19 years in an islanded microgrid is demonstrated. • A maximum yearly benefit of $ 1,344,107 is obtained due to deferred upgrades. • The annual costs, emission, and loss reduce by 28%, 32%, and 26%, respectively. [ABSTRACT FROM AUTHOR]