Dispatchable Distributed Generation Research Articles

Optimal Operation of Distribution Networks Considering Energy Storage Devices

This paper presents a mixed-integer second-order cone programing (MISOCP) model to solve the optimal operation problem of radial distribution networks (DNs) with energy storage. The control variables are the active and reactive generated power of dispatchable distributed generators (DGs), the number of switchable capacitor bank units in operation, the tap position of the voltage regulators and on-load tap-changers, and the operation state of the energy storage devices. The objective is to minimize the total cost of energy purchased from the distribution substation and the dispatchable DGs. The steady-state operation of the DN is modeled using linear and second-order cone programing. The use of an MISOCP model guarantees convergence to optimality using existing optimization software. A mixed-integer linear programing (MILP) formulation for the original model is also presented in order to show the accuracy of the proposed MISOCP model. An 11-node test system and a 42-node real system were used to demonstrate the effectiveness of the proposed MISOCP and MILP models.

Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids

This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology.

Stochastic‐based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads

Microgrids with different technologies in distributed generations (DGs), different control facilities and power electronic interfaces require proper management and operation strategies. In these strategies, in order to reach the optimum scheduling, the stochastic nature of some decision variables should be considered. Subsequently, it will lead to a decrease in the forced load curtailment and an increase in the economic efficiency from the perspective of both the maingrid and the microgrid owner. In this study, the availability of dispatchable DGs, energy storage, renewable energy sources (RESs) and the maingrid as well as the power generation of RESs and load are studied through their uncertainty natures. For dealing with these uncertainties, stochastic variables computation module is designed which generates several scenarios by Monte Carlo simulation at each hour. The microgrid operation is optimised in uncertainty environment through a linear two-stage stochastic model. The stochastic scheduling model which is solved by mixed-integer linear programming is compared with a deterministic model through three different cases in presence of demand response on a sample microgrid. The results explicitly show benefits of the proposed stochastic model since it provides accuracy in scheduling and decreases the operation cost.

Hourly demand response and battery energy storage for imbalance reduction of smart distribution company embedded with electric vehicles and wind farms

This paper presents a new optimization framework to optimize the bidding strategy of a smart distribution company (SDC) in a day-ahead (DA) energy market. This SDC contains wind farms as stochastic DG units as well as plug-in electric vehicles (PEVs) as responsive loads. The intermittent nature of wind power may result in significant imbalance penalty costs for the SDC participated in the DA energy market. The proposed optimization framework uses the potential of plug-in electric vehicles (PEVs) and battery energy storage (BES) to manage possible imbalances of wind farms. In order to modify the charging pattern of PEVs, hourly electricity prices are calculated in the optimization framework and sent to PEV owners via smart communication system. PEV owners change their charging pattern in response to these hourly prices with the aim of reducing their electricity bills. In addition to responsive loads, BES and wind farms, the SDC also contains dispatchable distributed generators (DGs), distribution network and non-responsive loads. The two-point estimate method (TPEM) is used to model the uncertainties associated with wind farms power generation. Moreover, Benders decomposition technique (BDT) is implemented to simplify the optimization procedure. Finally, the effectiveness of the proposed framework is evaluated on several case studies.

Integration of nodal hourly pricing in day-ahead SDC (smart distribution company) optimization framework to effectively activate demand response

This paper focuses on using a new nodal hourly electricity pricing to maximize the profit of a LDC (local distribution company). The proposed pricing mechanism determines DA (day-ahead) hourly retail prices based on load specifications. These specifications include location, price elasticity and demand profile. Nodal hourly prices are determined in an optimization framework which schedules the LDC to bid optimally in the DA wholesale market. The LDC considered in this study is equipped with smart grid technology and known as SDC (smart distribution company). This SDC contains DERs (distributed energy resources) including dispatchable and non-dispatchable DGs (distributed generators), BES (battery energy storage) and price sensitive consumers. It can also exchange power with upstream network. The optimization framework considers constraints of DGs and BES as well as AC constraints of the distribution network. Moreover, welfare constraints of load are implemented to maintain customers' satisfaction. This framework determines the optimal bidding strategy of the SDC and nodal hourly prices for end consumers simultaneously in an iterative procedure. The BDT (Benders decomposition technique) with strong cuts is applied to simplify the optimization procedure. Finally, the effectiveness of the proposed strategy is evaluated on several case studies using real data from Ontario power market.

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.

Bilevel economic operation of distribution networks with microgrid integration

Traditionally, the economic operation of a microgrid (MG) is independent with that of a distribution network (DN), and the electricity trade between them is simply based on contract prices or wholesale electricity market prices. In order to coordinate the benefits of DNs and MGs more effectively, a new economic operation model for DNs with MG is proposed in this paper modeled as a bilevel programming problem (BLPP). At the upper level, electric power from the gird and electricity price of MGs are control variables. A DN seeks the least operation cost by optimizing these control variables. At the lower level, control variables are electric power of dispatchable distributed generations (DGs) and the DN. Based on the electricity price got from the upper level and the operation costs of dispatchable DGs, a MG minimizes its hourly operation cost by optimizing the control variables. Furthermore, electricity price constraints are considered in this BLPP model, and a method to dynamically determine the constraints is proposed. They are decided by the load power demand profile in the DN and the price differential between the wholesale electricity market and dispatchable DGs. A comprehensive real-time pricing for residential consumers is also considered in the operation model to study the impact from demand response. The proposed BLPP model is solved by particles swarm optimization based on bilevel iterative algorithm. The demonstration is performed on a modified IEEE 33-node radial distribution system.

A Hierarchical Framework for Generation Scheduling of Microgrids

The uncertainty and intermittency of renewable energy sources pose a challenge to generation scheduling of microgrids. This paper presents a hierarchical framework to handle the uncertainty and realize an economic generation schedule of microgrids. The lower level combines a battery energy-storage system (BESS) with renewable energy sources, targeting maximal utilization of renewable power and minimal deviation from the schedule, to provide an optimal generation plan in the day-ahead market. The upper level minimizes the total cost of the microgrid by the genetic algorithm (GA) to yield an economic generation plan of dispatchable distributed generators (DGs) based on the lower level. Two stages of such hierarchical scheduling before and in the day gradually reduce the uncertainty, and lead the overall schedule to evolve toward a stable and economic one. The method is tested on a 14-bus microgrid system. The simulation indicates that the wind turbine and photovoltaic are gradually stabilized by BESS Besides, the operation is scheduled economically, and cheap DGs are always arranged in priority.

An Analytical Approach for Reliability Evaluation of Distribution Systems Containing Dispatchable and Nondispatchable Renewable DG Units

With ever increasing penetration of renewable distributed generation (DG) in distribution systems, power restoration of remote distribution feeders under emergency conditions tends to be carried out with the support of renewable DG units. The available power from the renewable DG units ensures restoration of more number of affected customers, thus, improving overall system reliability. In this paper, a probabilistic based analytical method is developed to assess system reliability in terms of system average interruption duration index and system average interruption frequency index for distribution feeders containing dispatchable and nondispatchable renewable DG units. The proposed method has been developed by implementing DG side restoration with comprehensive technical considerations, including possible failures of DG units, time-dependent patterns of load demand and DG power output, and single-stage and two-stage restoration. The proposed analytical method is validated by comparing with the Monte Carlo simulation and results are presented.

An optimal allocation of distributed generation and voltage control devices for voltage regulation considering renewable energy uncertainty

Distributed generation (DG) may result in voltage fluctuation by changing line flow and reactive power injection, especially DG that generates power from renewable energy resources. To cope with this problem, this paper proposes an optimization process to optimally regulate the system voltage profile to lie close to the desired values by using the adaptive Tabu search (ATS) algorithm. The system voltages will be regulated by using dispatchable DG and voltage control devices, i.e. voltage regulator and capacitor. Moreover, probabilistic load flow calculation by using Monte Carlo simulation is chosen to evaluate the uncertainty of DG powered by renewable energy resources. The number of switching operations of the voltage regulator and capacitor are also accounted for in the optimization constraints, as excessive frequent switching operations can damage these devices. The optimal sizes and locations of dispatchable DGs and capacitors are considered as the optimization variables. The proposed method is demonstrated in an IEEE 34‐bus distribution test system and a modified 21‐bus Provincial Electricity Authority (PEA) system (Thailand). © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks

This paper proposes an algorithm for modeling stochastically dependent renewable energy based distributed generators for the purpose of proper planning of unbalanced distribution networks. The proposed algorithm integrate the diagonal band Copula and sequential Monte Carlo method in order to accurately consider the multivariate stochastic dependence between wind power, photovoltaic power and the system demand. Secondly, an efficient algorithm based on modification of the traditional Big Bang-Big crunch method is proposed for optimal placement of renewable energy based distributed generators in the presence of dispatchable distributed generation. The proposed optimization algorithm aims to minimize the energy loss in unbalanced distribution systems by determining the optimal locations of non-dispatchable distributed generators and the optimal hourly power schedule of dispatchable distributed generators. The proposed algorithms are implemented in MATLAB environment and tested on the IEEE 37-node feeder. Several case studies are done and the subsequent discussions show the effectiveness of the proposed algorithms.

Dynamic distributed generation dispatch strategy for lowering the cost of building energy

The practicality of any particular distributed generation installation depends upon its ability to reduce overall energy costs. A simple yet effective economic dispatch strategy with the goal to use distributed generation to minimize the cost of building energy is developed in this work. The strategy is designed to reduce the individual utility, operations and maintenance charges that increase the cost of energy. Various electric rate structures are modeled in detail and applied with the economic dispatch strategy to simulate meeting various measured building demand dynamics for heat and power. Using the economic dispatch strategy, various modes of operation, such as electric or thermal load following, peak shaving, peak shifting, or base-load operation are simulated. The economic dispatch strategy is compared to more traditional dispatch strategies to demonstrate its effectiveness in reducing total energy costs.

Leader-Follower Strategies for Energy Management of Multi-Microgrids

This paper presents the application of bilevel programming for analyzing competitive situations of hierarchical decision making between an Energy Services Provider representing several microgrids (MGs)-each one comprising controllable loads and dispatchable distributed generation units-and a large central production unit. The rules of the interaction between the two entities are determined in a bilateral contract. This operation is compared to the vertically integrated operation of this system, i.e., only one entity manages both the central production unit and the distributed resources of the MG. This comparison highlights the benefits of applying a two level structure in the simulated interaction.

Optimal placement of dispatchable and nondispatchable renewable DG units in distribution networks for minimizing energy loss

This paper presents a methodology for the integration of dispatchable and nondispatchable renewable distributed generation (DG) units for minimizing annual energy losses. In this methodology, analytical expressions are first proposed to identify the optimal size and power factor of DG unit simultaneously for each location for minimizing power losses. These expressions are then adapted to place renewable DG units for minimizing annual energy losses while considering the time-varying characteristics of demand and generation. A combination of dispatchable and nondispatchable DG units is also proposed in this paper. The proposed methodology has been applied to a 69-bus test distribution system with different scenarios. The results demonstrate that dispatchable DG units or a combination of dispatchable and nondispatchable DG units can lead to a substantial reduction in annual energy losses when compared to nondispatchable DG units. The results also show that a maximum annual energy loss reduction is achieved for all scenarios proposed with DG operation at optimal power factor.

Optimal distribution network reinforcement considering load growth, line loss, and reliability

A new comprehensive planning methodology is proposed for implementing distribution network reinforcement. The load growth, voltage profile, distribution line loss, and reliability are considered in this procedure. A time-segmentation technique is employed to reduce the computational load. Options considered range from supporting the load growth using the traditional approach of upgrading the conventional equipment in the distribution network, through to the use of dispatchable distributed generators (DDG). The objective function is composed of the construction cost, loss cost and reliability cost. As constraints, the bus voltages and the feeder currents should be maintained within the standard level. The DDG output power should not be less than a ratio of its rated power because of efficiency. A hybrid optimization method, called modified discrete particle swarm optimization, is employed to solve this nonlinear and discrete optimization problem. A comparison is performed between the optimized solution based on planning of capacitors along with tap-changing transformer and line upgrading and when DDGs are included in the optimization.

Controllable Harmonic Current Sharing in Islanded Microgrids: DG Units With Programmable Resistive Behavior Toward Harmonics

In order to obtain a coordinated approach to integrate the increasing presence of distributed generation (DG) units in the electric power system, the microgrid concept has been introduced. Since most DG units use a converter as an interface with the grid, new control strategies for these converters are being developed. For the islanded mode of the microgrid, with the voltage-based droop control strategy, active and reactive power balancing and sharing between multiple DG units are achieved. This method also makes it easy to delay changing the output power of the renewable DG units compared to that of the dispatchable DG units without communication. However, to deal with harmonic and nonlinear loads, the power control strategies in general, with the voltage-based control strategy as a specific example, need to be modified. Otherwise, the grid-forming DG units form short circuits for harmonic currents. Therefore, in this paper, the shunt harmonic impedance method for harmonic damping in the grid-connected mode is adapted for application in islanded microgrids. The voltage-based droop control strategy of the DG units is extended with programmable resistive behavior toward harmonics. In this way, harmonic current sharing between DG units can be achieved in a controllable manner (e.g., according to the ratings of the units).

Location and contract pricing of distributed generation using a genetic algorithm

This paper presents an approach based on a specialized genetic algorithm (GA) to determine the location and contract pricing of dispatchable distributed generation (DG) units in distribution systems. The proposed approach is based on a nonlinear bilevel programming framework that involves the interests of two different agents: the DG owner who procures the maximization of the profits obtained from the energy sales, and the Distribution Company (DisCo), which procures the minimization of the payments incurred in attending the forecasted demand. To meet the forecasted demand the DisCo can purchase energy either from the wholesale energy market or from the DG units within its network. The proposed GA determines both the location and contract pricing of the DG units that would render maximum profits to the DG owner, subject to the minimization of payments procured by the DisCo. To show the effectiveness of the proposed approach, several tests were carried out on a modified IEEE 34-bus and 85-bus distribution networks.

Optimal Contract Pricing of Distributed Generation in Distribution Networks

This paper proposes a bilevel programming approach to determine the optimal contract price of dispatchable distributed generation (DG) units in distribution systems. Two different agents are considered in the model, namely, the distribution company (DisCo) and the owner of the DG. The former seeks the minimization of the payments incurred in attending the forecasted demand, while the latter seeks the maximization of his profit. To meet the expected demand, the DisCo has the option to purchase energy from any DG unit within its network and directly from the wholesale electricity market. A traditional distribution utility model with no competition among DG units is considered. The proposed model positions the DG owner in the outer optimization level and the DisCo in the inner one. This last optimization problem is substituted by its Karush-Kuhn-Tucker optimality conditions, turning the bilevel programming problem into an equivalent single-level nonlinear programming problem which is solved using commercially available software. Tests are performed in a modified IEEE 34-bus distribution network.

Minimizing Energy Losses: Optimal Accommodation and Smart Operation of Renewable Distributed Generation

The problem of minimizing losses in distribution networks has traditionally been investigated using a single, deterministic demand level. This has proved to be effective since most approaches are generally able to also result in minimum overall energy losses. However, the increasing penetration of (firm and variable) distributed generation (DG) raises concerns on the actual benefits of loss minimization studies that are limited to a single demand/generation scenario. Here, a multiperiod AC optimal power flow (OPF) is used to determine the optimal accommodation of (renewable) DG in a way that minimizes the system energy losses. In addition, control schemes expected to be part of the future Smart Grid, such as coordinated voltage control and dispatchable DG power factor, are embedded in the OPF formulation to explore the extra loss reduction benefits that can be harnessed with such technologies. The trade-off between energy losses and more generation capacity is also investigated. The methodology is applied to a generic U.K. distribution network and results demonstrate the significant impact that considering time-varying characteristics has on the energy loss minimization problem and highlight the gains that the flexibility provided by innovative control strategies can have on both loss minimization and generation capacity.