Renewable Distributed Energy Resources Research Articles

Particle swarm optimisation with opposition learning-based strategy: an efficient optimisation algorithm for day-ahead scheduling and reconfiguration in active distribution systems

In operation of active electric distribution networks, optimal configuration and schedule of distributed generation and reactive power resources are determined. This represents a formidable multi-modal constrained optimisation problem with discrete decision variables. Metaheuristics are the most common approaches for solving this problem. However, due to its multi-modal nature, metaheuristics commonly converge prematurely into local optima and cannot find near-global solutions. In this research, a new particle swarm optimisation (PSO) variant is put forward for finding optimal configuration and schedule of distributed generation and reactive power resources in distribution systems including both dispatchable and renewable distributed energy resources. In the proposed PSO variant, opposition-based learning concept is incorporated into PSO which reduces premature convergence probability through enhancement of swarm leaders. The results of the proposed opposition-based PSO in IEEE 69 bus system indicate its outperformance over conventional PSO, time-varying acceleration coefficient PSO, fractal optimisation algorithm and evolutionary programming.

Microgrid protection under weather uncertainty using joint probabilistic modeling of solar irradiance and wind speed

The present power scenario has witnessed an unprecedented rise in the penetration level of renewable distributed energy resources (DERs). The wider acceptance of photovoltaic (PV) and wind-based DERs in microgrids poses severe protection challenges due to the uncertain variation in solar irradiance level and wind speed. The sporadic variation often results in underreach and overreach of overcurrent relays. With the aim of imparting robustness to the protection scheme against weather intermittency, a technique based on meteorological data-dependent joint probabilistic modeling of the stochastic variation in solar irradiance and wind speed has been integrated with a discrete wavelet transform (DWT) based feature extraction module and an optimal random forest (ORF) based classifier to perform fault detection/classification and zone identification. The performance of the proposed protection scheme has been examined for diverse test cases and compared with other computational intelligence techniques using numerical and real-time simulations.

Modeling and frequency control of community micro-grids under stochastic solar and wind sources

The Community Micro-Grid (CMG) is a coordinated local grid area served by one or more distribution substations supported by local renewable and other distributed energy resources (DER). In an islanded mode, the CMG stability reduces due to low inertia of the equivalent system and stochastic nature of renewable energy sources (RESs). To study the frequency stability of the islanded CMG system, this paper explores the mathematical modeling aspects of different sources in the CMG and the robust control design for the community microgrid model. The robust design using a fixed structure H∞ synthesis method has been presented to compensate for the effect of modeling uncertainty. The uncertainty in the CMG model results in parametric perturbation which degrades its performance and stability. This paper explores the existing mathematical models of CMG’s and different control techniques applied for frequency regulation. Robust controller design for CMG is proposed that can handle the stochastic input disturbances like a sudden change in power coming from solar and wind sources and also the model uncertainty that results in the parametric perturbations. The complete model is simulated using MATLAB Simulink platform and results of frequency deviation obtained by robust and PID controller are compared. The controller hardware in loop (C-HIL) validation of the robust control method for CMG has been done using a real-time controller board (DS1104). This CHIL system also implements the CMG plant-controller model and analyzes the frequency deviation against load and renewable power fluctuations. The oscillations in frequency deviation are analyzed for sudden rise or fall in the renewable power coming from the solar and wind sources. A reduction of 30% is achieved in frequency overshoot and settling time using a robust controller than the PID controller. The controller developed and implemented using H∞ synthesis has robust stability and performance for the perturbed CMG model and also shows a better time response, unlike the PID controller.

Graph Theory and Classifying Security Events in Grid Security Gateways

In recent years, the use of security gateways (SG) located within the electrical grid distribution network has become pervasive. SGs in substations and renewable distributed energy resource aggregators (DERAs) protect power distribution control devices from cyber and cyber-physical attacks. When encrypted communications within a DER network is used, TCP/IP packet inspection is restricted to packet header behavioral analysis which in most cases only allows the SG to perform anomaly detection of blocks of time-series data (event windows). Packet header anomaly detection calculates the probability of the presence of a threat within an event window, but fails in such cases where the unreadable encrypted payload contains the attack content. The SG system log (syslog) is a time-series record of behavioral patterns of network users and processes accessing and transferring data through the SG network interfaces. Threatening behavioral pattern in the syslog are measurable using both anomaly detection and graph theory. In this paper, it will be shown that it is possible to efficiently detect the presence of and classify a potential threat within an SG syslog using light-weight anomaly detection and graph theory.

Self Sustainable Power Solution for Home Appliance

With the significant concerns of carbon emission from the fossil fuels, global warming and energy crisis, the renewable distributed energy resources (DERs) are going to be integrated in the smart grid. The electrification of sectors such as heating and transportation are the challenges as well as an opportunity for distribution system operators. This paper proposes a prototype of a Self-Sustainable ERs to be implemented in Indian Context . The proposed system aims to reduce the reliance on non-renewable energy sources and emphasizes the importance of green energy as a main source of energy. Furthermore, it introduces responsiveness of the loads to trigger demand response. This paper will cover the development of the proposed system, and design a simple residential nano-grid as part of the operating system of the DER system.

A robust optimization approach for coordinated operation of multiple energy hubs

The energy hub (EH) has become an important concept in terms of optimizing multi-carrier energy systems’ efficiency and flexibility. In this regard, an optimal operation model for managing multiple EHs with electrical and heat energy demands is proposed, which aims to reduce the total cost of the EHs. The components of each EH include the combined heat and power (CHP) unit, gas boiler, heat storage (HS) unit, wind turbine (WT) and photovoltaic (PV) array. A robust optimization approach is then proposed to deal with the uncertainties of renewable distributed energy resources (RDERs) and electricity prices. Meanwhile, both electrical and thermal demand response (DR) programs are considered in the proposed model. Finally, the optimized dispatch results under three different scheduling scenarios, i.e., the independent scenario, and the aggregation scenarios with and without DR programs are discussed. The simulation results show that the proposed multiple EHs optimization model can effectively reduce the total cost of EHs. Meanwhile, the results also reveal that implementing the DR programs can further reduce the consumers’ total costs through load shifting.

Multi-agent approach to modeling and simulation of microgrid operation with vehicle-to-grid system

This study presents the modeling and simulation of a vehicle-to-grid (V2G) system within a microgrid considering the requirements of various components of the microgrid system such as distributed renewable energy resources (DERs), plug-in electric vehicles (PEVs) and non-PEV loads. The system modeling is carried out using an agent-based methodology where components of the microgrid are modeled as agents. The use of PEV car batteries collectively as a virtual power plant (VPP) enables PEVs to not only act as loads but as energy sources alongside DERs such as wind and solar power generation. The smart control and scheduling of the charging and discharging of the PEVs by the charging station can be used to achieve sustainable integration of a high number of PEVs in the microgrid power system. In addition to simulating a microgrid operation, results of this study show how agents’ behavior change based on factors such as penetration of renewable energy, penetration of PEVs, travel pattern of PEV drivers and price of energy generation.

Finding optimal combination of distributed renewable energy sources for virtual power plant with minimum output variance

In the current European electricity market, if the actual generation is less than the scheduled contract or excess generation is injected into the grid, a penalty for deviation may be applied to market participants. Virtual power plants (VPPs) are trying to improve their forecasting technologies for managing this risk and increasing their expected profit. However, in this study, the authors would choose another approach to reduce the risk. To minimise the variance of a sum of outputs of all distributed renewable energy resources (DRESs), the covariance between the output of each DRES should be minimised. This covariance depends on the correlation between the outputs of DRESs. If the DRESs in the same area are aggregated as a single VPP unit, the correlation would be high and the variance of the VPP will be large, accordingly. On the other hand, if the DRESs in separated regions are collected as one VPP unit, the correlation between DRESs and the variance of the VPP may be smaller than in the former case. Using this feature, in this study, the authors would find the optimal combinations of VPP compositions with a minimum weighted average of variance of m VPP units among N DRESs based on historical output data.

An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes

This paper presents an integrated tool to mitigate power quality issues in a microgrid through coordinating the operating schedule of its generating resources and loads. Such a microgrid includes renewable and conventional distributed energy resources, electric vehicles, energy storage, linear and nonlinear loads, while it serves as an example small-to-medium scale residential and commercial buildings. The proposed tool operates on a sequential, two-stage basis: at the first stage the energy management system (EMS) ensures that the microgrid’s generation resources and loads are dispatched at the minimum total system cost. In addition, it assesses the potential provision of flexibility services towards the system operator, relying on financially incentivized power signal requests. At the second stage, the power quality (PQ) framework evaluates whether the proposed optimal solution complies or not with several PQ standards applicable to the distribution level. The unique characteristic of the proposed tool is the self-triggered interaction between the EMS and the PQ framework, which identifies potential PQ violations, and restores the PQ indices to acceptable levels through an iterative process. Case studies have been performed with realistic model parameters to verify the performance of the proposed integrated tool. The obtained results demonstrate the effectiveness of the algorithm in managing voltage deviations, voltage unbalance, as well as harmonic distortions with a small additional cost for the total system.

Coordinated power system expansion planning considering the DSO's market operations

The main motivation of this study is to address the challenges due to high penetration of renewable distributed energy resources (DERs) and efficiently benefit from DERs in distribution system planning (DSP). This paper considers a decentralized enhanced platform for DSP which is coordinated with bulk power system planning (PSP) to keep the optimality and security of the whole power system. In the proposed coordinated approach, distribution system operators (DSOs) plan and operate DERs to upgrade their local distribution areas (LDAs), supply forecasting local load growth, and avoid or defer costly generation and transmission expansion planning at the bulk power system. The proposed DSP model consists of two main loops pertaining to the DSO's planning and operation in the first loop and the ISO's simulated operation in the second loop. The Benders decomposition (BD) is employed to iteratively solve the problems. The robust and stochastic programming are used for modelling uncertainties. The DSP problem is modelled as a mixed-integer linear problem (MILP). The CPLEX solver in GAMS is applied to the modified 24-bus IEEE RTS in a 10-year horizon for numerical validation. The results show the performance of the proposed approach in reducing the total cost of supplying growing loads.

Distributed renewable energy resource trust metrics and secure routing

To ensure reliable and predictable service in the electrical grid between distributed renewable distributed energy resources (DERs) it is important to gauge the level of trust present within critical components and DER aggregators (DERAs). Although trust throughout a smart grid is temporal and dynamically varies according to measured states, it is possible to accurately formulate communications and service level strategies based on such trust measurements. Utilizing an effective set of machine learning and statistical methods, it is shown that establishment of trust levels between DERAs using behavioral pattern analysis is possible. It is also shown that the establishment of such trust can facilitate simple secure communications routing between DERAs. Providing secure routing between DERAs enables a grid operator to maintain service level agreements to its customers, reduce the attack surface and increase operational resiliency.

Optimal planning of AC-DC hybrid transmission and distributed energy resource system: Review and prospects

With the highly-extensive integration of distributed renewable energy resources (DER) into the grid, the power distribution system has changed greatly in the structure, function and operating characteristics. On this ground, An AC-DC hybrid DER system becomes necessary for effective management and control over DER. This paper first summarizes the physical characteristics and morphological evolution of AC-DC hybrid DER system. The impact of these new features on system configuration planning is analyzed with respect to its flexible networking, rich operation control modes, and tight source-network-load-storage coupling. Then, based on a review of the existing research, problems and technical difficulties are figured out in terms of converter modeling, steady-state analysis, power flow calculation, operating scenarios management, and optimization model solution. In light of the problems and difficulties, a framework for the configuration optimization of AC-DC hybrid DER systems is proposed. At last, the paper provides a prospect of key technologies from six aspects including morphology forecasting, coupling interaction analysis, uncertainty modeling, operation simulation, optimization model solving algorithm and comprehensive scheme evaluation.

Innovative Volt/VAr Control Philosophy for Future Distribution Systems Embedded With Voltage-Regulating Devices and Distributed Renewable Energy Resources

Conventional voltage-regulating devices such as load tap changers, voltage regulators, and local capacitor banks are commonly used for voltage-regulation purposes in medium-voltage distribution systems. Although such devices exhibit excellent capability for Volt/VAr control (VVC) support, fine-tuning of their controller parameters and enactment of Volt/VAr support by renewable distributed generation (DG) units is essential for effective and efficient operation of future distribution systems embedded with wind and solar-Photo-voltaic (PV) units. In this paper, an innovative algorithm is conceptualized for control set-point re-adjustment of different VVC devices for coordinated voltage control in distribution systems. The intended VVC strategy is able to minimize the voltage variations including voltage drop and rise cases mainly caused by power output of renewable DG units that cannot be solved by voltage regulating devices due to their time-delayed operation. Moreover, the control algorithm is proposed to be implemented online using advanced distribution management system for effective voltage control. The simulation studies are carried out using the test distribution system derived from an electricity network in New South Wales, Australia. The simulation results show that voltage regulation can effectively be achieved in renewable rich future distribution systems by applying the proposed tuning algorithm, which has been designed based on a new VVC philosophy.

Hybrid Salp Swarm Algorithm for integrating renewable distributed energy resources in distribution systems considering annual load growth

Load growth in electrical distribution networks became naturally occurring due to industrial development and human population demand growth. Consequently, system losses are continually raised while the voltage profile is reduced. This paper presents a novel hybrid method to determine the best locations and sizes of single and multiple of different renewable distributed energy resources (DER). The presented hybrid method is based on Salp Swarm Algorithm (SSA) and combined power loss sensitivity (CPLS). Integration of photovoltaics (PV) and wind turbines (WT) in distribution network is used to enhance the system voltage, minimize system losses and increase the system capacity. The effect of annual load growth in system load and system operating constraints are taken in consideration. IEEE 33-bus and 69-bus radial distribution systems (RDS) are used to validate the presented algorithm for integrating the DERS in distribution system. In addition, the presented algorithm is compared with different recent optimization algorithms in order to prove its effectiveness and superiority.

Editorial: Introduction to the Special Section on Dynamic Modeling, System Identification, Analysis, and Control of Renewable Distributed Energy Resources for Grid Integration

Editorial: Introduction to the Special Section on Dynamic Modeling, System Identification, Analysis, and Control of Renewable Distributed Energy Resources for Grid Integration

Low voltage ride through capability enhancement in a grid‐connected wind/fuel cell hybrid system via combined feed‐forward and fuzzy logic control

Fault ride through (FRT) capability is an essential practice as per the present grid code demands for grid-connected renewable energy-based distributed energy resources. Studies on FRT capability for grid-connected hybrid systems are rarely found. This study considers a wind energy conversion system and a fuel cell system interconnected at a common dc bus. It proposes a new feed-forward-based FRT control scheme for the inverter control where new current references in dq -axis frame are derived by tracking the positive sequence power. The newly derived references are fed forward to the input of the current regulator of the voltage source inverter. Second, fuzzy logic-based current controllers are suggested to improve the tracking capability of the current references in the inverter control scheme so as to enhance the FRT capability of the hybrid system as a whole. The proposed feed-forward-fuzzy control scheme for achieving an enhanced FRT capability is compared with the conventional dq current control and feed-forward FRT control for various grid voltage sag tests, where the performance of the combined feed-forward-fuzzy control is found better. The validation of the proposed FRT control scheme is performed in MATLAB-Simulink environment.

Planning of High Renewable-Penetrated Distribution Systems Considering Complementarity and Cluster Partitioning

Photovoltaic (PV) and wind power (WT) resources can influence each other in some scenarios, and this influence tends to show that the rise of PV resources may indicate the drop of WT resources, and vice versa. This pattern of PV and WT resources influencing each other is called the complementary characteristics of PV and WT power. The complementary characteristics of the power outputs of different kinds of distributed renewable energy resources (DRERs) and the correlation between DRERs outputs and loads can impact the consumption of DRERs by the loads within the grid, which represents the rate of DRER outputs consumed by loads instead of being reduced. In this regard, this paper investigates a planning strategy for DRERs considering these two factors. An improved co-variance matrix method is applied to generate complementary samples of DRERs and correlated samples of DRERs and loads. The samples generated are used to study the impacts of the degree of correlation between DRERs and loads on the consumption ability of DRERs. The concept of the cluster is introduced as a region including DRERs with complementary characteristics. Based on the cluster partition method and the samples generated, the DRERs planning model is proposed to maximize the profits of different DRER stakeholders. The planning model is transformed into a single objective model through the ideal point method. A Benders decomposition-based method is developed to efficiently solve the proposed model, and an actual network in China is used to illustrate its performance. The results show DRER consumption can be significantly improved by the proposed planning model.

Distribution Electricity Pricing Under Uncertainty

Distribution locational marginal prices (DLMPs) facilitate the efficient operation of low-voltage electric power distribution systems. We propose an approach to internalize the stochasticity of renewable distributed energy resources (DERs) and risk tolerance of the distribution system operator in DLMP computations. This is achieved by means of applying conic duality to a chance-constrained AC optimal power flow. We show that the resulting DLMPs consist of the terms that allow to itemize the prices for the active and reactive power production, balancing regulation, and voltage support provided. Finally, we prove the proposed DLMPs constitute a competitive equilibrium, which can be leveraged for designing a distribution electricity market, and show that imposing chance constraints on voltage limits distorts the equilibrium.

Review of three-phase inverters control for unbalanced load compensation

<span>In the microgrid systems, three-phase inverter becomes the main power electronic interface for renewable distributed energy resources (DERs), especially for the islanded microgrids in which the power quality is easily affected by unbalanced and nonlinear loads, this is due to the fact that the voltage and frequency of the microgrid are not supported by the main power grid but determined only by the inverters. Therefore, the compensation of the load unbalances and harmonics in autonomous microgrid inverters are getting more attention in power quality research areas. The main purpose of this paper is to represent an overview of the control strategies of various inverters for unbalanced load compensation</span>

Integrated distribution system and urban district planning with high renewable penetrations

Recent efforts to reduce energy consumption and greenhouse gas emissions have resulted in the development of sustainable, smart districts with highly energy efficient buildings, renewable distributed energy resources (DERs), and support for alternative modes of transportation. However, there is typically little if any coordination between the district developers and the local utility. Most attention is paid to the district's annual net load and generation without considering their instantaneous imbalance or the connecting network's state. This presents an opportunity to learn lessons from the design of distribution feeders for districts characterized by low loads and high penetrations of DERs that can be applied to the distribution grid at large. The aim of this overview is to summarize current practices in sustainable district planning as well as advances in modeling and design tools for incorporating the power distribution system into the district planning process. Recent developments in the modeling and optimization of district power systems, including their coordination with multi‐energy systems and the impact of high penetration levels of renewable energy, are introduced. Sustainable districts in England and Japan are reviewed as case studies to illustrate the extent to which distribution system planning has been considered in practice. Finally, newly developed building‐to‐grid modeling tools that can facilitate coordinated district and power system design with utility involvement are introduced, along with suggestions for future research directions.This article is categorized under: Wind Power > Systems and Infrastructure Energy Policy and Planning > Systems and Infrastructure Energy Efficiency > Systems and Infrastructure