Non-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.

Multi-Power Carriers-Based Integrated Control for Series-Cascaded Microgrid

Series-cascaded microgrids (SCMGs) indeed provide control flexibility and high-voltage synthesis capabilities. However, the power distribution in SCMGs based on distributed generation (DG) sources stays understudied. This paper proposes an SCMG topology using non-dispatchable DG sources and battery energy storage, with an integrated power-routing control. The objective is to address power distribution limitations and stabilize SCMG output voltages under varying conditions. A case study validates the control methodology, considering zero irradiation levels for photovoltaic (PV) and maximum power sharing. The battery modules play a crucial role by providing power, voltage support, and maintaining capacitor voltage at a reference value of a PV-integrated module. This is achieved through a third harmonic current injection in the fundamental frequency current, coupled with proportional power distribution using a third harmonic power signal. The effectiveness of the proposed SCMG topology and control is demonstrated through MATLAB/Simulink and hardware-in-loop analyses (Typhoon HIL). The results present an extended power distribution between series-cascaded DG sources-based units while ensuring stable SCMG output voltages, even in adverse conditions like PV module intermittency. Future work aims to extend the proposed topology to a ring/delta-connection SCMG, where third harmonic current aids power distribution among SCMG legs and between series-cascaded DG sources-based units.

Providing a New Multiobjective Two-Layer Approach for Developing Service Restoration of a Smart Distribution System by Islanding of Faulty Area

One of the essential capabilities of a smart distribution network is to improve network restoration performance using the postfault islanding method. Islanding of the faulty area can be done offline and online. Online islanding will decrease load shedding and operation cost. In this study, a novel two-step mathematical method for system restoration after the fault is presented. A new mathematical model for the optimal arrangement of the system for the faulty area in the first layer is proposed. In this layer, the main objective is to decrease the distribution system’s load shedding and operational costs. In this regard, after the fault event, the boundary of the islanded MGs is determined. Then, in the second layer, the problem of unit commitment in the smart distribution network is addressed. In addition to the load shedding, optimal planning of energy storage systems (ESSs) and nondispatchable distributed generation (DG) resource rescheduling are also determined in this layer. The important advantages of the proposed approach are low execution time and operational costs. A demand response (DR) program has also been used for optimal system restoration. Solving the problem using the multiobjective method with the epsilon-constraint method is another goal of the paper, which simultaneously minimizes the cost and the emissions of the smart distribution network. The proposed model has been tested on an IEEE 33-bus system. Better performance of the proposed model compared to the techniques in the literature has been proven.

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.

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.

Optimizing Decision-Making of A Smart Prosumer Microgrid Using Simulation

Distributed renewable energy sources offer significant alternatives for Qatar and the Arab Gulf region’s future fuel supply and demand. Microgrids are essential for providing dependable power in difficult-to-reach areas while incorporating significant amounts of renewable energy sources. In energy-efficient data centers, distributed generation can be used to meet the facility’s overall power needs. This study primarily focuses on the best energy management practices for a smart microgrid in Qatar while taking demand-side load management into account. This article looked into a university microgrid in Qatar that primarily aimed to get all of its energy from the grid. While diesel generators are categorized as a dispatchable distributed generation with energy storage added to handle solar radiation from the sun and high grid power operating costs in the suggested scenario, wind turbines and solar Photovoltaic (PV) are classified as non-dispatchable distributed generators. The resulting linear math issues are assessed and displayed in MATLAB optimization software using a mixed-integer linear programming (MILP) strategy. According to the simulation results, the suggested energy management strategy reduced the university microgrid’s grid power costs by 38.8%, making it an affordable solution which is somehow greater than the prior case scenario’s 23% savings. The installed solar system capacity’s effects on the economy, society, and finances were also assessed, and it became clear that the best option for the smart microgrid was determined that would be 325 kW of solar PV, 25 kW of wind turbine, and 600 kW of diesel generators, respectively. Given the current situation, university administrators are urged to participate in distributed generators and adopt cutting-edge designs for energy storage technologies due to the significant environmental and financial benefits.

Reliability-Oriented Reconfiguration of Power Distribution Systems Considering Load and RES Production Scenarios

The ever-increasing integration of non-dispatchable distributed generation, i.e., renewable energy sources (RES), arises new challenges in the field of power system's reliability. Distribution network reconfiguration (DNR) is a cost-effective approach for the distribution system operator (DSO) that wishes to enhance system's reliability without infrastructure upgrades. This paper introduces a novel path-based mixed-integer second-order cone programming model to optimally solve the reliability-oriented DNR problem. The DSO's objectives that are optimized are: a) improvement of distribution system's reliability indices and b) minimization of power losses. The proposed model is enriched with a scenario-based stochastic programming formulation that considers multiple levels of load and RES production. The standard 33-nodes distribution system and a real-world 83-nodes distribution system are employed to prove the efficiency and applicability of the model. Firstly, the multi-objective nature of the reliability-oriented DNR problem is investigated by conducting a sensitivity analysis, which reveals a trade-off region between reliability indices and power losses. Moreover, the obtained results show different global optimal solutions when the variability of load and RES production is considered. This highlights the importance of considering a scenario-based approach for load and RES production when solving the reliability-oriented DNR problem.

Scheduling and Sizing of Campus Microgrid Considering Demand Response and Economic Analysis.

Current energy systems face multiple problems related to inflation in energy prices, reduction of fossil fuels, and greenhouse gas emissions which are disturbing the comfort zone of energy consumers and the affordability of power for large commercial customers. These kinds of problems can be alleviated with the help of optimal planning of demand response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of the green distributed generators (DGs). To this end, a real-time based campus is considered that currently takes provision of its loads from the utility grid only. According to the proposed given scenario, it will contain solar panels and a wind turbine as non-dispatchable DGs while a diesel generator is considered as a dispatchable DG. It also incorporates an energy storage system with optimal sizing of BESS to tackle the multiple disturbances that arise from solar radiation. The resultant problem of linear mathematics was simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of energy management (EMS) minimizes the grid electricity costs by 668.8 CC/day ($) which is 36.6% of savings for the campus microgrid. The economic prognosis for the campus to give an optimum result for the UET Taxila, Campus was also analyzed. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs was also determined. The substantial environmental and economic benefits compared to the present situation have prompted the campus owners to invest in the DGs and to install large-scale energy storage.

Multistage Planning Model for Active Distribution Systems and Electric Vehicle Charging Stations Considering Voltage-Dependent Load Behavior

This work proposes a novel mixed-integer linear programming model for the medium-term multistage planning of active distribution systems and electric vehicle charging stations (EVCSs). Investment alternatives include the installation of conductors, capacitor banks, voltage regulators, dispatchable and nondispatchable distributed generation, energy storage units, and EVCSs. Hence, the model identifies the best size, location, and installation time for the candidate assets under the uncertainty associated with electricity demand, energy prices, renewable energy sources, and EVCSs’ load profiles. Unlike classical planning approaches, conventional load is modeled as voltage-dependent. Besides, EVCSs are planned by zones to optimize the coverage of the service provided to users of electric vehicles and to reduce the discrepancy between the geographical requirements and the optimal locations for the installation of EVCSs in the system. EVCSs’ load profiles are calculated using a travel simulation algorithm based on real travel patterns that consider fast, slow, and residential chargers. Moreover, as another salient feature, constraints for CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions are incorporated into the model. The resulting model is formulated as a stochastic scenario-based program, which is driven by the minimization of the total expected cost. Tests are conducted using a 69-node system to demonstrate the effectiveness of the proposed model.

Optimal day ahead energy consumption management in grid‐connected microgrids

The day-ahead scheduling of microgrids in the presence of nondispatchable distributed generators (DGs) is a challenging task for microgrid operators. The valve point loading problem of distributed generators and its effect on input-output characteristics is not extensively covered in the published scientific literature on microgrids. In this research work, the day-ahead scheduling problem of microgrids is formulated in the presence of DGs with a nonconvex cost function. First, the flexible load-shaping based demand-side management strategy is adopted to reduce the peak loads and enhance the DG's unit operational costs. The impact of demand-side management and price-driven demand response programs on convex and nonconvex energy management system (EMS) problems is investigated. Furthermore, the short-term scheduling horizon of 15-minutes resolution time is considered for both solar and wind power to maintain forecast accuracy. The state-of-art optimization algorithm of quantum particle swarm optimization is devised to solve the proposed problem in the presence of the nonconvex cost function of DGs. The technical performance indices for each demand response program is evaluated, and the best alternative demand response program is chosen by implementing analytical hierarchy process. The proposed algorithm efficiently solves the nonconvex EMS problem, and the simulation results yield a 12.11% reduction in operating cost without compromising customer satisfaction. Computational time, convergence characteristics, and solution effectiveness in contrast to recently reported metaheuristic algorithms are examined for the effectiveness of the suggested algorithm.

Medium-term planning of active distribution systems considering voltage-dependent loads, network reconfiguration, and CO2 emissions

This work proposes a novel mixed-integer linear programming model to address the medium-term reinforcement planning for active distribution networks, taking into account multiple investment options and CO2 emission limits. The investment plan jointly includes (i) the replacement of overloaded conductors, (ii) the installation of voltage control equipment such as voltage regulators and capacitor banks, and (iii) the installation of distributed energy resources, such as dispatchable and non-dispatchable renewable generators, and energy storage units. Uncertainties associated with the demand for electricity, energy prices at the substation, and non-dispatchable distributed generation are addressed through scenario-based stochastic optimization. In contrast to conventional planning methods, the proposed approach models the load as voltage-dependent in order to achieve substantial reductions in energy consumption. As another outstanding feature, network reconfiguration, which is an operational planning alternative that is normally addressed independently, is incorporated within the planning options. The objective function of the model is aimed at establishing an investment strategy with minimal total costs, but that satisfies the operational restrictions of the network and CO2 emissions cap. A 69-node system was used to test the proposed model and, the results show that modeling the load as voltage-dependent and integrating network reconfiguration into the medium-term planning actions helps to achieve an effective network that, in addition to being environmentally friendly, has low total planning costs. Finally, the scalability of the proposed method was evaluated using a real 2313-node system.

Optimal siting of solar based distributed generation (DG) in distribution system for constant power load model

Abstract In this paper optimal location and sizing of Non-dispatchable Distributed Generation (NDG) based distributed generation (DG) is investigated by considering uncertainty of NDG. In present study a solar photovoltaic (SPV) is considered as NDG because SPV based DG has uncertain nature of generated power because it is powered by solar irradiance which has uncertain characteristics. To investigate the uncertainty of intermittent nature of solar irradiance Beta Probability Density Function (BPDF) based PDF is considered for addressing the uncertainty of solar insolation. For determining the optimal size of DG unit an analytical based methodology is developed. In this study optimal capacity of NDG is estimated by deriving the expressions of DG unit at each bus. Further, a multi-objective index factor (MIF) is designed in order to find out the candidate bus for DG placement. The proposed technique is applied on IEEE 33-bus distribution network. The obtained results reveal that proposed technique provide almost similar result to that of other method which is available in literature.

Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

Present power systems face problems such as rising energy charges and greenhouse gas (GHG) releases. These problems may be assuaged by participating distributed generators (DGs) and demand response (DR) policies in the distribution system (DS). The main focus of this paper is to propose an energy management system (EMS) approach for campus microgrid (µG). For this purpose, a Pakistani university has been investigated and an optimal solution has been proposed. Conventionally, it contains electricity from the national grid only as a supply to fulfil the energy demand. Under the proposed setup, it contains campus owned nondispatchable DGs such as solar photovoltaic (PV) panels and microturbines (MTs) as dispatchable sources. To overcome the random nature of solar irradiance, station battery has been integrated as energy storage. The subsequent nonlinear mathematical problem has been scheduled by mixed-integer nonlinear programming (MINLP) in MATLAB for saving energy cost and battery aging cost. The framework has been validated under deterministic and stochastic environments. Among random parameters, solar irradiance and load have been taken into consideration. Case studies have been carried out considering the demand response strategies to analyze the proposed model. The obtained results show that optimal management and scheduling of storage in the presence of DGs mutually benefit by minimizing consumption cost (customer) and grid load (utility) which show the efficacy of the proposed model. The results obtained are compared to the existing literature and a significant cost reduction is found.

A new voltage regulation strategy using developed power sharing techniques for solar photovoltaic generation-based microgrids

Integration of solar photovoltaic (PV) sources to power grid is increasing rapidly in recent years. Since the PV source is an intermittent source, this causes many challenges to distribution network. To overcome these challenges, a voltage regulation strategy using a developed power management technique for microgrid system is proposed. The technique is based on voltage ride through capability. The active and reactive power flow along with the voltage profile has been deeply investigated to improve the stability of the distribution network. As a testing environment, three microgrid configurations developed in MATLAB/Simulink have been investigated. The study shows a faster response time and lower circulating current in configuration 2. Moreover, it revealed the effectiveness of the power management technique when a cluster of dispatchable and non-dispatchable distributed generators is in operation.

Optimal Scheduling for Campus Prosumer Microgrid Considering Price Based Demand Response

Existing energy systems face problems such as depleting fossil fuels, rising energy prices and greenhouse gas (GHG) emissions which seriously affect the comfort and affordability of energy for large-sized commercial customers. These problems may be mitigated by the optimal scheduling of distributed generators (DGs) and demand response (DR) policies in the distribution system. The focus of this paper is to propose an energy management system (EMS) strategy for an institutional microgrid (μG) to reduce its operational cost and increase its self-consumption from green DGs. For this purpose, a real-time university campus has been considered that is currently feeding its load from the national grid only. However, under the proposed scenario, it contains building owned solar photovoltaic (PV) panels as non-dispatchable DG and diesel generator as dispatchable DG along with the energy storage system (ESS) to cope up with the intermittency of solar irradiance and high operational cost of grid energy. The resulting linear mathematical problem has been mapped in mixed-integer linear programming (MILP) and simulated in MATLAB. Simulations show that the proposed EMS model reduces the cost of grid electricity by 35% and 29% for summer and winter seasons respectively, while per day reductions in GHG emissions are 750.46 kg and 730.68 kg for the respective seasons. The effect of a half-sized PV installation on energy consumption cost and carbon emissions is also observed. Significant economic and environmental benefits as compared to the existing case are enticing to the campus owners to invest in DG and large-scale energy storage installation.

Proposing a Stochastic Model for Failures of the Upstream Grid While Optimally Scheduling of Microgrid

In this paper, optimal scheduling of home appliancesas well as dispatchable distributed generators are determinedwithin a residential microgrid for a specific day. In this regard,stochastic nature of non-dispatchable distributed generators suchas wind and solar is modeled taking advantages of Markov model.Also, there are other uncertainties in the model stem from time offailure as well as duration of failure in the upstream grid which istaken into account in the problem formulation. The effectivenessof the proposed method is shown by minimizing cost of providingcustomers with electricity in a residential microgrid.

Active Power Sharing and Frequency Recovery Control in an Islanded Microgrid With Nonlinear Load and Nondispatchable DG

This paper discusses the effect of nonlinear loads of some equipment on the islanded microgrids. The nonlinear load with active and reactive power is considered, which highly threatens the stability of the microgrids. Moreover, a wind turbine, which is a nondispatchable distributed generation (DG), is included as a power fluctuation source in the microgrid. The maximum power point tracking is integrated to ensure that the wind turbine generator operates at its maximum power level. In order to enable efficient power sharing among DG units, active power–frequency droop control is adopted for a microgrid with three DG units. In particular, a single input interval type-2 fuzzy integral (SI-IT2-FI) controller is implemented for the self-frequency recovery control of the microgrid to restore the frequency deviation within the desired range from its nominal value. In this way, by varying an adjustable design parameter which shapes the footprint of uncertainty, the SI-IT2-FI controller with different control surfaces generation is developed. In order to ascertain the efficiency of the SI-IT2-FI controller, the output responses in the absence and presence of wind turbine are compared with the P–f droop control. Total harmonic distortion is adopted as the harmonic metric to investigate the impact of the nonlinear loads at the test microgrid. Finally, in order to ascertain the feasibility and applicability of the proposed technique, a software-in-the-loop real-time simulation using dSPACE 1202 board is carried out. The simulation outcomes of the islanded microgrid confirm the efficiency of the proposed scheme.

Sizing and placement of battery-coupled distributed photovoltaic generations

The rate of integration of distributed generation (DG) using renewable sources is increasing in distribution networks because of their technical and economic advantages. However, mismatches in the timing of electricity demand and generation in distribution networks have restricted the penetration of non-dispatchable DGs such as photovoltaic (PV) plants. Such a problem associated with the integration of DGs in distribution networks could be eliminated using the proper application of battery energy storage systems. In this paper, optimal planning of battery-coupled distributed photovoltaic generators (BCDPGs) in distribution networks is presented. The optimal planning determines the size and location of BCDPGs and schedules the charging and discharging of the batteries, while minimizing the total energy losses subject to technical constraints. To estimate the output from PV modules, 15-year solar irradiance data is modeled using the beta probability density function. Mixed-integer optimization using a genetic algorithm is employed for solving the optimization problem. The numerical studies on a 33-node distribution network show the advantages of the proposed methodology. The results obtained through this study show the capability of the proposed method to optimally allocate BCDPGs to largely improve the quality of supply of power from non-dispatchable DGs in terms of reducing energy losses, improving bus voltages, and increasing penetration levels of renewable energy. The proposed approach can also be useful when applied to realistic networks for proper sizing and operations of battery-coupled photovoltaic plants.

Networked microgrids for service restoration in resilient distribution systems

This study presents a novel networked microgrid (MG)-aided approach for service restoration in power distribution systems. This study considers both dispatchable and non-dispatchable distributed generators (DGs), and energy storage systems. The uncertainty of the customer load demands and DG outputs are modelled in a scenario-based form. A stochastic mixed-integer linear program is formulated with the objective to maximise the served load, while satisfying the operation constraints of the distribution system and MGs. The interaction among MGs is modelled using the type 1 special ordered set. Two approaches are developed and compared: (i) a centralised approach where all MGs are controlled by a distribution system operator, and (ii) a decentralised approach where the distribution system and MGs are managed by different entities. The proposed restoration models are tested on a modified IEEE 123-bus distribution system. The results demonstrate the advantages of leveraging networked MGs to facilitate service restoration.

Techno-economic and environmental approach for optimal placement and sizing of renewable DGs in distribution system

This paper presents an approach for optimal placement and sizing of dispatchable and non-dispatchable renewable distributed generators (DG) units in a radial distribution network. In this work, wind and solar energy based DG units are considered as non-dispatchable DG, while biomass energy based DG units are considered as dispatchable. The formulated multi-objective problem comprises of technical performance indices for real power loss, maximum branch current capacity, voltage deviation, environment impact reduction index; and economic index. The appropriate weighting factors for different indices has been decided by Analytic Hierarchy Process (AHP). To solve the developed formulation, Particle Swarm Optimization (PSO) based approach has been used and applied on a 51-bus distribution networks. Also, in order to validate the use of AHP, the prioritization to different indices leads to formation of four scenarios and results are obtained in terms of the technical, economic and environmental parameters. The results obtained by the proposed method have been compared with other multi-objective problems considering different weighting factors to the indices and emerged as a simple and efficient approach.