Dispatchable Distributed Generation Research Articles

Distributed Generation and Demand Response Coordination for Optimal Operation of Microgrid Using Modified Elephant Herd Optimization

The optimal operation and coordination of distributed generation (DG) sources and demand response (DR) programs help to minimize the operating cost of microgrids along with other mutual benefits. The model is developed, and the multiobjective problem subjected to the set of constraints is solved by using the modified elephant herd optimization (MEHO) technique in this work. The expected output of the model is the optimal generation scheduling plan of both the dispatchable and non-dispatchable DG and optimized cost for the isolated microgrid operation. The other objectives related to the environmental aspects and reliability are also considered for optimization. DG operation is coordinated with the day ahead DR programs to minimize the costs while satisfying the operation limits of the DG sources and consumer demand. The novelty of the work lies in finding the optimal solution considering the multiobjective for optimal DG operation and analyzing the impact of different types of DR programs on microgrid operation by the MEHO technique. The effectiveness of the proposed model and validity of the algorithm is further tested on a 14 bus microgrid benchmark system. Through simulation and optimization results it is observed that the proposed algorithm has less computational time and better accuracy.

Optimal size and location of dispatchable distributed generators in an autonomous microgrid using Honey Badger algorithm

Optimal size and location of dispatchable distributed generators in an autonomous microgrid using Honey Badger algorithm

Retracted: Resiliency Enhancement of Distribution Power Grids Using Mobile Marine Power Source

In this paper, a new configuration security framework is developed for reliability and resiliency improvement of distribution networks by the coordination of the security-constraint unit commitment (SCUC) and mobile marine power sources (MMPSs) under both post-disaster and normal restoration operations. It is assumed that a MMPS contains non-dispatchable distributed generators (DGs), e.g., photovoltaics (PV), as well as dispatchable DGs, e.g., gas turbines and diesel generators. A mixed-integer linear programming model is formulated for coordinating MMPSs and SCUC under both normal and extreme conditions (e.g., natural disasters, cyber and physical attacks). To better characterize the uncertainties in renewable generation and electric load, a deep learning gated recurrent unit is adopted to forecast the PV power output. The proposed model is tested on the IEEE 69-bus distribution network to validate the effectiveness and merits of MMPSs. Results show that reliability and resiliency could be enhanced by using MMPSs within the network.

Allocation and sizing of dispatchable distributed generators considering value addition in resiliency and sustainability of power delivery infrastructure

ABSTRACT The increasing power demand growth and extreme weather conditions push utilities to search for a more sustainable and resilient power distribution infrastructure. Dispatchable distributed generators (DGs) can contribute to achieving the goals. This paper reveals a general framework for allocating dispatchable DGs with their suitable location, capacity, and type in the test distribution networks to bring sustainability and resilient planning synergy. A multiobjective optimization approach has been presented to minimize payback year (PBY) for DGs, enhance the network’s operational efficiency, and promote system resiliency by satisfying various operating constraints. The proposed planning model is structured to give the best possible solution in compliance with changes in seasonal peak demands. The efficacy of the proposed scheme has been tested on a 28-bus Indian distribution system and an IEEE 33-bus test system, and results concerning the number of customers served, and priority customers restored are presented.

Comprehensive multi-benefit planning of sustainable interconnected microgrids

A comprehensive stochastic planning model with multi-objective, integrated sizing and scheduling strategies for distributed energy resources is fabricated and implemented for interconnected grid-tied microgrids in the present work. The optimum capacities of the renewable and dispatchable distributed generators, together with battery energy storage systems in the interconnected microgrids, are ascertained using grey wolf optimizer in the fuzzy domain with the objectives of minimizing the yearly expenses, emissions and energy loss, and maximizing the yearly monetary benefit acquired due to the deferral of network upgrades. The optimal dispatch of multi-energy resources is intended to satisfy price-sensitive, multi-energy demands economically and sustainably under the influence of a price-based demand response strategy. The proposed multi-microgrid planning framework, which is hinged on probabilistic power flow with the probabilistic modelling of renewable and load uncertainties, is targeted to benefit multiple entities, including the utility and microgrid prosumers. The effectiveness of the proposed formulation is demonstrated by the mitigation of the annual expenses, emissions, and energy loss by 51%, 70%, and 56%, respectively, and the deferral of the need for the relevant upgrades by 12 years. Moreover, the performance of the integrated resource allocation and scheduling model is also endorsed using environmental sustainability factors.

A new continuous variables and convex relaxation based formulation for reconfiguration of unbalanced mutually coupled active distribution networks

With the integration of renewable energy-based sources and demand response initiatives, the distribution network has transformed into an active network with bidirectional flows. Network Reconfiguration (NR), i.e., changing network topology, is an alternative to minimize power loss in the distribution network with sectionalizing switches (normally closed) and tie-line switches (normally open). Due to the discrete nature of switches (on or off), the NR formulation is generally a mixed-integer NP-hard nonlinear problem. This paper proposes the first NR formulation for an unbalanced active distribution network with continuous variables to indicate switches’ statuses in NR while considering mutual coupling between line impedances. The obtained solution provides the optimal real power and voltage magnitude settings of dispatchable Distributed Generators (DGs) and network topology for the minimum active power loss in the network. The proposed formulation considers all practical aspects of the distribution network, i.e., mutual coupling, load unbalances, angular unbalance, and the absence of one or two phases in a line section, which, otherwise, have not been considered together in previous formulations. Further, a novel convex relaxation for bus voltage phase angles avoids explicit voltage recovery. Compared to prior formulations, numeric results for unbalanced radial distribution networks showcase the proposed formulation’s effectiveness.

Power system resilience against climatic faults: An optimized self-healing approach using conservative voltage reduction

The resilience of power systems is critical in mitigating faults caused by the impending effects of climate change. Typical operational methods, such as network reconfiguration, may be insufficient to mitigate faults in the event of a contingency. On the other hand, reducing system demand may help increase the number of restored loads. As a result, this paper proposes a novel self-healing optimization approach based on a power grid concept known as conservative voltage reduction (CVR), which results in system demand reduction. The proposed optimization model is formulated as a mixed integer non-linear (MINLP) problem to fulfill an objective of minimizing unserved loads within the system. Voltage, thermal capacity, system radiality, and several more constraints have been taken into consideration while formulating the proposed model to handle both grid-connected and isolated modes of operation. Both dispatchable and non-dispatchable distributed generators (DGs) are taken into consideration. Dispatchable DGs are assumed to be capable of switching back and forth between constant power (PQ) and droop controls for the purpose of grid following and grid forming in grid-connected and isolated modes of operation, respectively. The proposed optimization approach is coded and solved using the General Algebraic Modeling System (GAMS) software. The IEEE 69-bus power distribution test system is utilized to test the validity and superiority of the proposed model. The results show that the proposed model effectively increases the system resilience by reducing the unserved power/energy within the system following a contingency.

Assessment of topological boundaries for microgrids in an active power distribution network during emergencies

Microgrid (MG) operation has become an intriguing possibility to enhance the sustainability, reliability, and resilience of electricity distribution systems in emergencies. However, identifying suitable physical boundaries of MGs from an active power delivery infrastructure is a highly challenging task. This study describes a novel approach for determining the topological boundary of multiple MGs to restore power supply in a power distribution system during the unavailability of grid power supply. This method tries to enhance the self-adequacy of the MGs by increasing power flow within the MG boundaries. In this context, possible demand at the nodes of the power delivery infrastructure during an emergency is anticipated by an autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network-based time series forecast models. A modified backward-forward sweep (MBFS) power flow analysis, tailored for islanded mode, is employed to assess the power flow status in the MGs during emergencies. The MGs are supplied with locally available dispatchable distributed generators (DDGs). A particle swarm optimization (PSO) algorithm intertwined with the MBFS power flow method has been employed to determine the boundary of MGs by identifying minimum power flow line in the shortest path between the consecutive DDGs. Further, this study demonstrates the evaluation of the shortest path between paths between the consecutive DDGs with the help of a breadth-first search (BFS) algorithm. The proposed strategy has been tested on a modified 33-bus, 69-bus and 118-bus power distribution test systems. The experimented results are verified with the results obtained from similar existing approaches to explore the opportunities in the proposed methodology.

Optimal Distributed Generation Planning Considering Economic and Operational Aspects

Distributed generation (DG) has gained prominence in distribution systems in recent decades due to its capability to reduce carbon footprint, improve power quality, voltage support and maintain a sustainable system. Although DG has numerous benefits, unplanned DG integration can adversely impact the power system parameters and the distribution company’s (Discom) income. This work addresses optimal site and sizing planning for renewable and dispatchable DG, considering long-term economic and technical benefits. A multi-objective (MO) problem is formulated to reduce the cost associated with DG units, their operation and maintenance (OM), fuel, emission, and operational objectives. The impacts of hourly variation in the power generation of renewable DG units and load demand profiles are investigated through case studies. Annual load growth and load modeling are included in the study to have a pragmatic model. An improved particle swarm optimization (IPSO) technique having chaotically varying inertia weight is used to determine the optimal solution. Profit and payback period (PP) calculations for the purchase and sale of electricity on the contract price for the project duration are discussed in detail. The proposed approach has been demonstrated to be effective through simulation studies conducted on the modified IEEE-33 bus and the real Italian distribution system.

A Distributed Coordination Control for Islanded Hybrid AC/DC Microgrid

Poor power sharing of hybrid ac/dc microgrid leads to the inefficient operation of distributed generators (DGs). Besides, the lack of inertia caused by droop and phase-locked loop-based current control brings negative effects to the system. In this article, we propose a distributed normalized power coordination (NPC) embedded with virtual synchronous generator for hybrid microgrid. The proposed NPC controller can achieve cross inertia support, accurate global power sharing, and frequency/voltage recovery among dispatchable DGs simultaneously. Furthermore, to suppress frequency and power oscillation, the index of inertia deviation is proposed to guide the inertia placement of hybrid MG based on Participation Factor analysis. Finally, the proposed strategy is validated by the simulation and hardware-in-loop tests compared to the conventional methods.

Fostering restoration and power delivery efficiency by distributed generators and line switches in electricity distribution network

ABSTRACT The distribution of power is being faced with challenges during and after extreme weather events. This contingent might cause a partial or full power blackout in the distribution territory. Hence, the restoration of the service is one of the key concerns of the utilities. A strategy is adopted to felicitate the power restoration process by the deployment of distributed generators (DGs) and line switches. A fuzzy-based system model is proposed to allocate line switches considering the impact of wind-driven extreme weather and the fault frequency character of the lines. The study further recommends an arrangement of dispatchable DGs in the sections of the distribution grid. A particle swarm optimization-based model is proposed for suitable capacity and location assessment of the DGs to foster the restoration of supply avoiding voltage and frequency disturbances in the developed sections of the grid. The results are further verified with the application of the grasshopper optimization algorithm on the multipurpose composite objective function. The proposed DG allocation approach escalates the network power loss reduction by 95.10%, improvement in average voltage stability level by 6.46%, and loadability margin enhancement by 37.37% when the DGs are operated in grid-connected mode. Moreover, power supply reliability is restored with successive employment of DG units. The proposed approach has been tested on an Indian 28-bus power distribution network. The outcome of the proposed approach is compared with existing DG allocation methods. The experiment confirms the credential of the proposed approach with better value addition in multiple objectives.

Probabilistic optimal power dispatch in a droop controlled islanded microgrid in presence of renewable energy sources and PHEV load demand

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.

Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load

The increasing integration of distributed resources, such as distributed generations (DGs), energy storage systems (ESSs), and flexible loads (FLs), has ushered in a new era for the active distribution system (ADS), characterized by more reliable, economical, and low-carbon. Nonetheless, with the increase in number and variety, how to realize self-consistent and self-optimal operation among these distributed resources has become major challenge for ADS. In this paper, a multi-agent deep reinforcement learning (MADRL) based algorithm with strategic goals of the real-time optimal scheduling of ADS is proposed, in which the uncertainty of renewable generations (RDGs), loads and electricity price are considered. The control variables contain the active and reactive power of dispatchable thermal DGs, the reactive power of photovoltaic and wind turbine DGs, the exchange power of ESSs, and the demand response (DR) of FLs. Besides, the region ownership of distributed resources is considered in our MADRL framework to resolve the partitioned optimization problem in large-scale ADS. Finally, the effectiveness and superiority of the proposed algorithm are demonstrated on the 33-node and 152-node active distribution system, including the terms of cost-effective and uncertainty adaptation.

An MILP Model for Switch, DG, and Tie Line Placement to Improve Distribution Grid Reliability

Remote controlled switches (RCSs) have the ability to isolate the faulted area from other parts of the distribution system. On the other hand, the dispatchable distributed generators (DDGs) and tie lines can supply the interrupted loads after fault occurrence trough microgrids and reduce the outage time. In this regard, this article proposes a planning model for simultaneous placement of RCSs, DDGs, and tie lines to improve distribution system reliability. The presence of renewable distributed generations (RDGs) and energy storage systems, which have an increasing penetration in today's distribution networks are also considered. Moreover, two different practical load shedding methods are considered to balance the total generation and consumption in microgrids. The predetermined expansion plan of RDGs is also considered. To simplify the implementation of the proposed optimization model and ensure the global optimal solution, the planning model is formulated as a mixed integer linear programming model which can be solved by various commercial solvers. Finally, the effectiveness of the proposed model is illustrated by implementation on bus 4 of Roy Billinton test system through various case studies and sensitivity analyses. The results demonstrate the significant impact of the proposed planning model on improving the distribution system reliability.

MILP Model for Optimal Day-Ahead PDS Scheduling Considering TSO-DSO Interconnection Power Flow Commitment Under Uncertainty

We propose a comprehensive framework for the optimal day-ahead scheduling of power distribution systems (PDS), based on a mixed-integer linear programming (MILP) model. The interaction between transmission system operator (TSO) and distribution system operator (DSO) is considered, where TSO informs DSO of the day-ahead forecast concerning the expected power flow (PF) commitment at the TSO-DSO interconnection points and the violation costs. We propose a MILP model to determine the optimal voltage and power settings of distributed energy resources (DERs), such as dispatchable distributed generators and energy storage units and optimal adjustments of capacitor banks controlled by current. The objective function includes the minimization of energy losses, voltage violations, power curtailment of DERs using volt-watt strategy, and violation of TSO-DSO interconnection PF commitment. Furthermore, we propose a method to estimate the degree of uncertainty in the PF commitment. Therefore, the proposed method can help achieve an optimal operation of the distribution system; in addition, the TSO can best model uncertainty at TSO-DSO interface points and thereby procure reduced amounts of resources to address these uncertainties. Numerical results obtained for a system based on real data highlight the several potential applications of the proposed framework.

Operational Issues of Contemporary Distribution Systems: A Review on Recent and Emerging Concerns

Distribution systems in traditional power systems (PS) constituted of passive elements and the distribution issues were then limited to voltage and thermal constraints, harmonics, overloading and unbalanced loading, reactive power compensation issues, faults and transients, loss minimization and frequency stability problems, to name a few. Contemporary distribution systems are becoming active distributed networks (ADNs) that integrate a substantially increasing amount of distributed energy resources (DERs). DERS include distributed generation (DG) sources, energy storage resources and demand side management (DSM) options. Despite their evidenced great benefits, the large-scale deployment and integration of DERs remain a challenge as they subsequently lead to the network operational and efficiency issues, hampering PS network reliability and stability. This paper carries out a comprehensive literature survey based on the last decade’s research on operational challenges reported and focusing on dispatchable and non-dispatchable DGs grid integration, on various demand response (DR) mechanisms and, on battery energy storage system (BESS) charging and discharging challenges, with the aim to pave the way to developing suitable optimization techniques that will solve the coordination of multiple renewable sources, storage systems and DRs to minimize distribution systems’ operational issues and thus improve stability and reliability. This paper’s findings assist the researchers in the field to conduct further research and to help PS planners and operators decide on appropriate relevant technologies that address challenges inherent to DG grid integration.

An efficient and reliable scheduling algorithm for unit commitment scheme in microgrid systems using enhanced mixed integer particle swarm optimizer considering uncertainties

The use of an electrical energy storage system (EESS) in a microgrid (MG) is widely recognized as a feasible method for mitigating the unpredictability and stochastic nature of sustainable distributed generators and other intermittent energy sources. The battery energy storage (BES) system is the most effective of the several power storage methods available today. The unit commitment (UC) determines the number of dedicated dispatchable distributed generators, respective power, the amount of energy transferred to and absorbed from the microgrid, as well as the power and influence of EESSs, among other factors. The BES deterioration is considered in the UC conceptualization, and an enhanced mixed particle swarm optimizer (EMPSO) is suggested to solve UC in MGs with EESS. Compared to the traditional PSO, the acceleration constants in EMPSO are exponentially adapted, and the inertial weight in EMPSO decreases linearly during each iteration. The proposed EMPSO is a mixed integer optimization algorithm that can handle continuous, binary, and integer variables. A part of the decision variables in EMPSO is transformed into a binary variable by introducing the quadratic transfer function (TF). This paper also considers the uncertainties in renewable power generation, load demand, and electricity market prices. In addition, a case study with a multiobjective optimization function with MG operating cost and BES deterioration defines the additional UC problem discussed in this paper. The transformation of a single-objective model into a multiobjective optimization model is carried out using the weighted sum approach, and the impacts of different weights on the operating cost and lifespan of the BES are also analyzed. The performance of the EMPSO with quadratic TF (EMPSO-Q) is compared with EMPSO with V-shaped TF (EMPSO-V), EMPSO with S-shaped TF (EMPSO-S), and PSO with S-shaped TF (PSO-S). The performance of EMPSO-Q is 15%, 35%, and 45% better than EMPSO-V, EMPSO-S, and PSO-S, respectively. In addition, when uncertainties are considered, the operating cost falls from $8729.87 to $8986.98. Considering BES deterioration, the BES lifespan improves from 350 to 590, and the operating cost increases from $8729.87 to $8917.7. Therefore, the obtained results prove that the EMPSO-Q algorithm could effectively and efficiently handle the UC problem.

Enhanced Artificial Immune Systems and Fuzzy Logic for Active Distribution Systems Reconfiguration

Nowadays, the high penetration of automation on smart grids challenges electricity companies in providing an efficient distribution networks operation. In this sense, distribution system reconfiguration (DSR) plays an important role since it may help solve real-time problems. This paper proposes a methodology to solve the DSR problem using artificial immune systems (AIS) based on a new, efficient, and robust approach. This new methodology, called Enhanced Artificial Immune Systems (EAIS), uses the values of the currents in wires for intelligent mutations. The problem is accomplished by a multi-objective optimization with fuzzy variables, minimizing power losses, voltage deviation, and feeders load balancing. A comparison with other DSR solution methods is presented. The method is compared with two other previously proposed methods with the help of the 33-bus, 84-bus, and 136-bus distribution systems. Different scenarios are analyzed, including the optimal location of the Distributed Generation (DG). The results show the applicability of the proposed algorithm for the simultaneous solution of DSR and location or dispatch of DGs.

A strategy to enhance the distribution systems recoverability via the simultaneous coordination of planning actions and operational resources

This paper presents a planning and operational strategy to improve the recoverability of distribution systems (DSs) to deal with a set of possible line fault scenarios. The strategy simultaneously optimizes the allocation of dispatchable distributed generation (DG) units while coordinating a dynamic restoration process based on a radial topology reconfiguration, an islanding operation, a demand response program, and the pre-positioning and dispatch of mobile emergency storage units. The uncertainty and variability associated with solar irradiation and demand are captured via a multi-period formulation based on a stochastic mixed-integer linear programming model. The objective function of this model minimizes the investment cost of new dispatchable DG units and the amount of energy shedding within the system. Simulations are performed on adapted 33-node and 53-node test systems to validate the proposed strategy under four different test conditions, numerical results reveal the advantages of simultaneously solving the planning and operational stages to improve the recoverability of the system.

A risk-based dispatchable distributed generation unit and tie line planning framework to improve the resilience of distribution systems

Due to the increasing global warming caused by greenhouse gas emissions, it is anticipated that the number and severity of natural disasters such as hurricanes, wildfires, and floods will increase in the coming years. In this regard, this paper presents a planning framework for distribution systems to improve their resilience against natural disasters. A mathematical model is developed to simultaneously determine the optimal locations of tie lines and dispatchable distributed generation (DDG) units. Two different types of faults, i.e., open circuit and short circuit faults, are considered in the planning model to better simulate the natural disasters. Presence of renewable DGs including photovoltaic cells (PVs) and wind turbine (WTs), as well as energy storage systems (ESSs) are also considered. The conditional value at risk (CVaR) which is a well-known risk index is used to manage the system risk. The problem is formulated as a mixed integer linear programming (MILP) model, which can be solved using various commercial solvers and achieve the global optimal solution. Finally, to illustrate the effectiveness of the proposed model on improving the resilience of distribution systems and manage the system risk, it is implemented on IEEE 33 bus test system using various case studies and sensitivity analyses.