Dispatchable Distributed Generation Units Research Articles

EXPERIMENTAL ASSESSMENT OF CONSUMER UNBALANCE AND NONLINEARITY EFFECTS IN THREE-PHASE NETWORKS WITHOUT NEUTRAL CONDUCTOR

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

THE EFFECTS OF HYBRID SLIDING MODE LEARNING CONTROL AND FEEDFORWARD ANGLE DROOP CONTROLLER WITH HIGH DROOP GAIN IN HYBRID MICROGRID WITH LOAD UNCERTAINTY AND NONLINEARITY

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

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.

PV hosting capacity assessment in distribution systems considering resilience enhancement

This paper presents an innovative strategy to assess the photovoltaic (PV)-based distributed generation (DG) hosting capacity considering the operation under normal and emergency conditions of electrical distribution systems (EDSs). In the emergency condition, the proposed strategy aims to improve the recoverability of EDSs against a set of high-impact fault scenarios. This recoverability process is achieved by the optimal coordination of topology reconfiguration, islanding operation of dispatchable DG units, and pre-positioning and displacement of mobile DG (MDG) units. This problem is formulated as a two-stage stochastic formulation, where the first one defines the DG hosting capacity and the amount of MDG units to be positioned in staging locations. Meanwhile, the second stage simulates high-impact fault events and, by applying resilience alternatives, the EDS recoverability can be improved. Inherently, the two-stage stochastic formulation is represented by a mixed-integer linear programming (MILP) model. The objective function of this MILP model maximizes the installed PV-based DG capacity while the amount of energy load shedding after fault events is minimized. To validate and show the scalability of the proposed strategy, two EDS are studied under different high-impact fault events and considering the application of multiple resilience alternatives. Results show that by estimating the capacity of PV-based DG simultaneously with the restoration process, the number of pre-positioned and dispatched MDG units can be reduced. On the other hand, when this PV capacity is determined disregarding fault scenarios, this solution could lead to unviable conditions and, thus, generation curtailment of up to 80% could be required.

Improvement of the Distribution Systems Resilience via Operational Resources and Demand Response

This article presents a restoration approach for improving the resilience of electric distribution systems (EDSs) by taking advantage of several operational resources. In the proposed approach, the restoration process combines dynamic network reconfiguration, islanding operation of dispatchable distributed generation units, and the prepositioning and displacement of mobile emergency generation (MEG) units. The benefit of exploring a demand response (DR) program to improve the recoverability of the system is also taken into account. The proposed approach aims to separate the in-service and out-of-service parts of the system while maintaining the radiality of the grid. To assist the distribution system planner, the problem is formulated as a stochastic-scenario-based mixed-integer linear programming model, where uncertainties associated with PV-based generation and demand are captured. The objective function of the problem minimizes the amount of energy load shedding after a fault event as well as PV-based generating curtailment. To validate the proposed approach, adapted 33-bus and 83-bus EDSs are analyzed under different test conditions. Numerical results demonstrate the benefits of coordinating the dynamic network reconfiguration, the prepositioning and displacement of MEG units, and a DR program to improve the restoration process.

Day-ahead energy management and feeder reconfiguration for microgrids with CCHP and energy storage systems

Abstract Energy management featuring distribution feeder reconfiguration (DFR) and reactive power control, improves the technical and economic efficiency of microgrids. The present work proposes a framework that leverages scenarios to jointly manage the real and reactive power dispatches of the controllable generation resources as well as the topology of the distribution feeder. Multiple operation measures are optimized including the operation cost, real power loss, the voltage stability index (VSI), and the greenhouse gas emissions of the microgrid. Hybrid Big Bang- Big Crunch (HBB-BC) algorithm is used to solve the formulated optimization problem. Non-dispatchable and dispatchable distributed generation units (DGs), as well as the battery and thermal energy storage systems (BESS and TESS), are considered as a hybrid energy system. Combined cooling, heating, and power (CCHP) units are considered as dispatchable DGs and wind and solar photovoltaic generations are considered as non-dispatchable DGs. The efficiency of the proposed model and solution algorithm is investigated using a 33-bus microgrid, and the simulation outcomes are discussed.

Incorporating flexibility requirements into distribution system expansion planning studies based on regulatory policies

Increasing penetration of renewable energy sources with intermittent generation calls for further flexibility requirements for efficient as well as the safe operation of power systems. Considering the significant growth of distributed energy resources in distribution systems, a promising approach to fulfill such requirements is to deploy local flexibility sources at the distribution level. Nonetheless, due to the monopoly nature of electricity distribution business, effective regulations are required to direct distribution companies toward fulfilling such goals. Accordingly, this paper aims at proposing various policies to motivate distribution companies to enhance the flexibility of their networks. In order to assess the effectiveness of these rules, we present a novel multi-stage distribution expansion planning model considering flexibility requirements. In this model, installation of conventional dispatchable distributed generation units and battery energy storage systems, as well as demand response programs, are considered available flexibility sources for distribution system planners. The proposed framework is applied to a test distribution network with 18 nodes, and the obtained results are thoroughly discussed. Finally, a sensitivity analysis is conducted to assess the effects of the key parameters of the proposed model on expansion planning of the test system.

Optimal sizing of energy storage system in islanded microgrid using incremental cost approach

Abstract This work presents a method for optimal sizing of a battery-based energy storage system (BESS) in a droop controlled islanded microgrid (DCIMG). The proposed method checks the economic feasibility of installing a given battery unit and finds the optimal size of BESS which minimizes the running cost. The optimal sizing problem is successfully integrated into the economic dispatch (ED) problem and solved for various cases considering inductive and resistive coupled droop controlled distributed generation (DG) units. A new heuristic method is proposed for optimal charging of battery units. The proposed method is compared with the Dynamic Programming (DP) method. Both algorithms use incremental cost data. The proposed method is applied to a 33 node system. The results show an almost linear relationship between the optimal size and efficiency of the BESS. The results also show that the BESS absorbed most of the load variation and the dispatchable DG units experienced fewer load changes. Installation of BESS resulted in a significant reduction in operating cost of the DCIMG.

Stability Constrained Economic Operation of Islanded Droop-Controlled DC Microgrids

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.

A stochastic mixed-integer convex programming model for long-term distribution system expansion planning considering greenhouse gas emission mitigation

This paper proposes a multistage convex distribution system planning model to find the best reinforcement plan over a specified horizon. This strategy determines planning actions such as reinforcement of existing substations, conductor replacement of overloaded feeders, and siting and sizing of renewable and dispatchable distributed generation units. Besides, the proposed approach aims at mitigating the greenhouse gas emissions of electric power distribution systems via a monetary form. Inherently, this problem is a non-convex optimization model that can be an obstacle to finding the optimal global solution. To remedy this issue, convex envelopes are used to recast the original problem into a mixed integer conic programming (MICP) model. The MICP model guarantees convergence to optimal global solution by using existing commercial solvers. Moreover, to address the prediction errors in wind output power and electricity demands, a two-stage stochastic MICP model is developed. To validate the proposed model, detail analysis is carried out over various case studies of a 34-node distribution system under different conditions, while to show its potential and effectiveness a 135-node system with two substations is used. Numerical results confirm the effectiveness of the proposed planning scheme in obtaining an economic investment plan at the presence of several planning alternatives and to promote an environmentally committed electric power distribution network.

Optimal Restoration/Maintenance Switching Sequence of Unbalanced Three-Phase Distribution Systems

This paper presents a mixed-integer non-linear programming (MINLP) model for the optimal restoration/maintenance switching sequence of unbalanced three-phase electrical distribution systems. Once the protection coordination has identified and cleared a faulty zone, the proposed MINLP model determines the status of remotely controlled switches and the dispatchable distributed generation (DG) units, used to de-energized the troubled section of the network and supply as much load as possible. The restoration considers the switching sequence over a discrete horizon, guaranteeing that the operational constraints of the distribution system are not violated in every step of the sequence. Furthermore, a set of linearization strategies are presented to transform the proposed MINLP model into a mixed-integer linear programming (MILP) model. The use of MILP models guarantees convergence to optimality by applying convex optimization techniques. Tests are performed on an unbalanced three-phase radial distribution system consisting of 123 nodes, 12 switches, and three dispatchable DG units. The obtained results show that the proposed optimization model is a holistic procedure that can be used to efficiently manage power restoration or to minimize isolated areas in case of scheduled maintenance in modern electrical distribution systems.

Optimal Operation of Droop-Controlled Islanded Microgrids

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.

Optimal operation of a droop‐controlled DCMG with generation and load uncertainties

This study presents a method for determining optimal droop settings of dispatchable distributed generation units in a droop-controlled microgrid (DCMG). The objectives are to (i) minimise the operational cost and (ii) minimise the emission in the DCMG while meeting all the operational constraints. The proposed formulation takes into account the electricity demand, load uncertainties and renewable power uncertainties in the MG. Load and renewable power uncertainties are modelled by Hong's point estimate method. The bi-objective optimisation problem is solved using fuzzified particle swarm optimisation. The proposed method is validated on a 6-bus DCMG test system. The results show the effectiveness of the proposed method.

Volt-VAr Control and Energy Storage Device Operation to Improve the Electric Vehicle Charging Coordination in Unbalanced Distribution Networks

In this paper, a new approach is presented to solve the electric vehicle charging coordination (EVCC) problem considering Volt-VAr control, energy storage device (ESD) operation and dispatchable distributed generation (DG) available in three-phase unbalanced electrical distribution networks (EDNs). Dynamic scheduling for the EVCC is proposed through a step-by-step methodology, which solves a mixed integer linear programming (MILP) problem for the whole time period. The objective is to minimize the total cost of energy purchased from the substation and DG units, the cost of energy curtailment on electric vehicles, the cost of energy injected from the ESDs, and the cost of energy curtailment on the ESDs. The Volt-VAr control considers the management of on-load tap changers, voltage regulators, and switchable capacitors installed along the grid. Furthermore, the formulation takes into account the voltage dependence of the loads, while the steady-state operation of the unbalanced distribution systems is modeled using linear constraints. The proposed model was tested in a 178-node three-phase unbalanced EDN considering a one-day time period.

Two-level decentralized optimization power dispatch control strategies for an islanded microgrid without communication network

Summary In this paper, two-level decentralized optimization power dispatch control strategies are proposed in an islanded microgrid without communication network to ensure frequency security and economic benefit. First, the secondary-level decentralized optimization control strategy is designed only by using local measurement regarding frequency deviation. It is able to implement the optimal power dispatch for all dispatchable distributed generation units in a coordinated way in order to completely restore the system frequency with minimum operation cost. Then, the first-level decentralized droop compensation control strategy is designed to implement real-time optimal power sharing among all energy storage units in order to quickly compensate large supply–demand imbalance and ensure frequency security. Moreover, the proposed control strategies are proved to be able to meet the requirements of the “plug and play” operation in the absence of communication network. Simulation results verify the validity of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

A tractable two-step MILP–QCP approach to on-line thermal constraint management in large radial active distribution systems

This paper focuses on centralized thermal overload management in active radial distribution systems which accommodate a significant amount of distributed generation (DG). The approach looks for minimizing the amount of dispatchable DG units curtailment to remove thermal overload while considering the possibility to use remotely controlled switches (RCSs) so as to reduce the amount of curtailed generation. This task is formulated as a mixed integer quadratically constrained programming (MIQCP) problem. In order to break-down the onerous computational burden of this MIQCP problem to runtimes compatible with real-time application to large distribution networks, a tractable two-step approach is proposed. The proposed approach consists in decomposing the original MIQCP problem into a mixed integer linear programming (MILP) approximation and a quadratically constrained programming (QCP) problem. We prove the interest and feasibility of the proposed approach, on a snapshot basis, in three distribution grid models of 34, 137 and 1089 nodes, respectively. Results show that the proposed approach scales much better with the problem size than the original MIQCP approach and provides solutions of comparable quality.

Impacts of direct cyber‐power interdependencies on smart grid reliability under various penetration levels of microturbine/wind/solar distributed generations

One of the major challenges in reliability evaluation of smart grid is the direct cyber-power interdependencies (DCPIs) impacts. The less effort has been devoted in the literature to investigate the DCPIs impacts, particularly in stochastic simulating space. This study proposed a novel methodology to determine the risk assessment of smart grid due to DCPIs under various distributed generation (DG) technology scenarios. The presented method is applied to a realistic distribution grid. The several sensitivity studies are performed to investigate the pattern of DCPIs impacts as DG penetration level in various scenarios. The test results show that regardless of the DG technology scenario, the DCPIs impacts gradually increase while the DG penetration increases, and it saturates when the penetration exceeds a certain level. The numerical result implies that the scenarios having more effective performance from the reliability improvement perspective such as those consist of dispatchable DG units such as microturbine are more affected due to DCPIs.

A bi-level approach for optimal contract pricing of independent dispatchable DG units in distribution networks

Distributed Generation (DG) units are increasingly installed in the power systems. Distribution Companies (DisCo) can opt to purchase the electricity from DG in an energy purchase contract to supply the customer demand and reduce energy loss. This paper proposes a framework for optimal contract pricing of independent dispatchable DG units considering competition among them. While DG units tend to increase their profit from the energy purchase contract, DisCo minimizes the demand supply cost. Multi-leader follower game theory concept is used to analyze the situation in which competing DG units offer the energy price to DisCo and DisCo determines the DG generation. A bi-level approach is used to formulate the competition in which each DG problem is the upper-level problem and the DisCo problem is considered as the lower-level one. Combining the optimality conditions ofall upper-level problems with the lower level problem results in a multi-DG equilibrium problem formulated as an equilibrium problem with equilibrium constraints (EPEC). Using a nonlinear approach, the EPEC problem is reformulated as a single nonlinear optimization model which is simultaneously solved for all independent DG units. The proposed framework was applied to the Modified IEEE 34-Bus Distribution Test System. Performance and robustness of the proposed framework in determining econo-technically fare DG contract price has been demonstrated through a series of analyses.

Fuzzy logic based coordinated control of battery energy storage system and dispatchable distributed generation for microgrid

Microgrid is a good option to integrate renewable energy sources (RES) into power systems. In order to deal with the intermittent characteristics of the renewable energy based distributed generation (DG) units, a fuzzy-logic based coordinated control strategy of a battery energy storage system (BESS) and dispatchable DG units is proposed for the microgrid management system (MMS). In the proposed coordinated control strategy, the BESS is used to minimize active power exchange at the point of common coupling of the microgrid for grid-connected operation, and is used for frequency control for island operation. The efficiency of the proposed control strategy was tested by case studies using DIgSILENT/PowerFactroy.