Reliability assessment of distribution networks with optimal coordination of distributed generation, energy storage and demand management

This article belongs to the Section F: Electrical EngineeringModern power distribution networks assume the connection of Distributed Generators (DGs) and energy storage systems as well as the application of advanced demand management techniques. After a network fault these technologies and techniques can contribute individually to the supply restoration of the interrupted areas and help improve the network reliability. However, the optimal coordination of control actions between these resources will lead to their most efficient use, maximizing the network reliability improvement. Until now, the effect of such networks with optimal coordination has not been considered in reliability studies. In this paper, DGs, energy storage and demand management techniques are jointly modelled and evaluated for reliability assessment. A novel methodology is proposed for the calculation of the reliability indices. It evaluates the optimal coordination of energy storage and demand management in order to reduce the energy-not-supplied during outages. The formulation proposed for the calculation of the reliability indices (including the modelling of optimal coordination) is described in detail. The methodology is applied to two distribution systems combining DGs, energy storage and demand management. Results demonstrate the capability of the proposed method to assess the reliability of such type of networks and emphasise the impact of the optimal coordination on reliability.This research was funded by the research program "Energy and Environment 2018" of Fundación Iberdrola, project name "SinCortes"

Operation optimization of a multi-energy microgrid under uncertainties

Multi-energy microgrids (MEMG) can simultaneously distribute and supply electrical and thermal energy to customers. This thesis work proposes an optimal coordinated dispatch method to minimize the operating costs of a MEMG when multiple uncertain parameters are considered. The operation method was modeled as a single-stage stochastic programming problem. The results demonstrated that the proposed operation model can achieve robust solutions when uncertainties are modeled accurately

Incentive-Based Expansion Planning and Reliability Enhancement Models for Smart Distribution Systems

Due to the rapid progress toward the implementation of smart grid technologies, electric power distribution systems are undergoing profound structural and operational changes. Climate concerns, a reduction in dependency on fossil fuel as a primary generation source, and the enhancement of existing networks constitute the key factors in the shift toward smart grid application, a shift that has, in fact, already led power industry stakeholders to promote more efficient network technologies and regulation. The results of these advances are encouraging with regard to the deployment and integration of small-scale power generation units, known as distributed generation units (DGs), within distribution networks. DGs are capable of contributing to the powering of the grid from distribution or even sub-distribution systems, providing both a positive effect on network performance and the least adverse impact on the environment. Smart grid deployment has also facilitated the integration of a variety of investor assets into power distribution systems, with a consequent necessity for positive and active interaction between those investors and local distribution companies (LDCs). This thesis proposes a novel incentive-based distribution system planning (IDSP) model that enables an LDC and DG investors to work collaboratively for their mutual benefit. Using the proposed model, the LDC would establish a bus-wise incentive program (BWIP) based on long-term contracts, which would encourage DG investors to integrate their projects at the specific system buses that would benefit both parties. The model guarantees that the LDC will incur minimum expansion and operation costs while concurrently ensuring the feasibility of DG investors’ projects. The proposed model also provides the LDC with the opportunity to identify the least-cost solution among a combination of the proposed BWIP and traditional expansion options (i.e., upgrading or constructing new substations, upgrading or constructing new lines, and/or reconfiguring the system). In this way, the model facilitates the effective coordination of future LDC expansion projects with DG investors. To derive appropriate incentives for each project, the model enforces a number of economic metrics, including the internal rate of return, the profit-investment ratio, and the discounted payback period. All investment plans committed to by the LDC and the DG investors for the full extent of the planning period are then coordinated accordingly. The intermittent nature of both system demand and wind- and PV-based DG output power is handled probabilistically, and a number of DG technologies are taken into account. Several linearization approaches are applied in order to convert the proposed model into a mixed integer linear programming (MILP) model, which is solved using a CPLEX solver. Reliability of service in a deregulated power environment is considered a major factor in the evaluation of the performance of service providers by consumers and system regulators. Adhering to imposed obligations related to the enhancement of overall system reliability places a substantial burden on the planning engineer with respect to investigating multiple alternatives and evaluating each option from both a technical and an economical perspective. This thesis also proposes a value-based reinforcement planning model for improving system reliability while maintaining reliability metrics within allowable limits. The optimal allocation of tie lines and normally open switches is determined by this planning model, along with required capacity upgrades for substations and lines. Two hierarchical levels for system operation under contingencies, namely, the restoration process and islanding-based modes, are applied in the model. A probabilistic analytical model is proposed for computing distribution system reliability indices based on consideration of these two hierarchical operating levels and taking into account variations in system demand, DG output power, and the uncertainty associated with system components. Due to the nature and complexity of these kinds of problems, a metaheuristic technique based on a genetic algorithm (GA) is implemented for solving this model. This thesis also proposes a new iterative planning model for smart distribution systems in which system reliability is considered a primary component in the setting of incentive prices for DG owners. A new concept, called generation sufficiency for dynamic virtual zones, is introduced in the model as a means of enhancing reliability in areas that are subject to reliability issues. To avoid any contravention of operational security boundaries, DG capacity is represented by two components: normal DG operating capacity and reserve DG capacity. The MILP planning model is constructed in a GAMS environment and solved with the use of a CPLEX solver

Development of a control framework for hybrid renewable energy system in microgrid

Electrical energy has an essential role in society as it ensures high quality of life and steady economic development. Demand for the electric energy has been steadily growing throughout the recent history and this demand is expected to grow further in the future. Most of electrical energy nowadays is generated by burning fossil fuels and there are serious concerns about the resulting emission. Renewable energy sources appeared as a viable alternative for environmentally hazardous sources. However, sources of renewable energy have considerably unpredictable and environmental conditions dependent power output and as such can’t be directly incorporated into existing electrical grid. These sources are usually integrated to the electrical grid as part of microgrid or hybrid energy source that consists of two or more energy sources, converters and/or storage devices. In hybrid energy sources, generation and storage elements complement each other to provide high quality and more reliable power delivery. This area of research is its infant stage and requires a lot of research and development effort to be done. Main objective of this thesis is to develop a framework for analysis and control of power electronics interfaces in microgrid connected hybrid energy source. The framework offers the generalized approach in treatment of control problem for hybrid energy sources. Development of the framework is done for the generalized hybrid source comprised of energy source(s), storage element(s), power electronic interfaces and control system. The main contributions of this thesis are, generalization of control problem for power electronics interfaces in hybrid energy source, the development of switching algorithm for three phase switching converters based on the closed loop behavior of the converters and the development of a maximum power point tracking algorithm for the renewable energy sources

Coordinated control of wind power and energy storage

Nowadays, wind power has become one of the fastest growing sources of electricity in the world. Due to the inherent variability and uncertainty, wind power integration into the grid brings challenges for power systems, particularly when the wind power penetration level is high. The challenges exist in many aspects, such as reliability, power quality and stability. With the rapid development of energy storage technology, the application of Energy Storage System (ESS) is considered as an effective solution to handle the aforementioned challenges. The main objective of this study is to investigate the coordinated control of wind power and ESS. Due to the different technical characteristics, such as power and energy density, ESS can play different roles either in generation-side, grid-side or demand side. This thesis focuses on the following two scenarios:• Scenario 1: As a part of wind farm, the ESS plays a generation-side role which aims to improve the grid-friendliness of the wind farm. • Scenario 2: As a part of microgrid, the ESS is used to efficiently accommodate the wind power fluctuation.Around the main objective, the relevant research fields including the wind turbine modeling and control, wind farm modeling and control, planning of ESS are also studied in this thesis. The implementation and validation of the International Electrotechnical Commission (IEC) generic Type 1A are presented in this thesis. It is shown that the implemented IEC generic Type 1 models in PowerFactory (PF) can represent the relevant dynamics during normal operation and fault conditions. The model against measurements validation was carried out to verify the implemented wind turbine generator model. For the wind turbine control strategy, the L1 adaptive controller for Maximum Power Point Tracking (MPPT) of a small variable speed Wind Energy Conversion System (WECS) is developed. It showed good tracking performance towards the optimum Tip Speed Ratio (TSR) and robustness with fast adaptation to uncertainties and disturbances. For the wind farm control, the optimal active power control based on Distributed Model Predictive Control (D-MPC) is proposed. With the developed D-MPC, most of computation tasks are distributed to the local D-MPCs equipped at each actuator (wind turbine or ESS). This control structure is independent from the scale of the wind farm. The algorithms for optimal siting and sizing of ESS in the grid with a significant penetration of wind power are studied and implemented in a test network. For the point of view the grid operator, the optimal sizing and siting of ESS are analyzed, which enhance the controllability and derive the global benefit of the whole grid

Improving Grid Hosting Capacity and Inertia Response with High Penetration of Renewable Generation

To achieve a more sustainable supply of electricity, utilizing renewable energy resources is a promising solution. However, the inclusion of intermittent renewable energy resources in electric power systems, if not appropriately managed and controlled, will raise a new set of technical challenges in both voltage and frequency control and jeopardizes the reliability and stability of the power system, as one of the most critical infrastructures in the today’s world. This dissertation aims to answer how to achieve high penetration of renewable generations in the entire power system without jeopardizing its security and reliability. First, we tackle the data insufficiency in testing new methods and concepts in renewable generation integration and develop a toolkit to generate any number of synthetic power grids feathering the same properties of real power grids. Next, we focus on small-scale PV systems as the most growing renewable generation in distribution networks and develop a detailed impact assessment framework to examine its impacts on the system and provide installation scheme recommendations to improve the hosting capacity of PV systems in the distribution networks. Following, we examine smart homes with rooftop PV systems and propose a new demand side management algorithm to make the best use of distributed renewable energy. Finally, the findings in the aforementioned three parts have been incorporated to solve the challenge of inertia response and hosting capacity of renewables in transmission network

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

Management Of Plug-In Electric Vehicles And Renewable Energy Sources In Active Distribution Networks

Near 160 million customers in the U.S.A. are served via distribution networks (DNs). The increasing penetration level of renewable energy sources (RES) and plug-in electric vehicles (PEVs), the implementation of smart distribution technologies such as advanced metering/monitoring infrastructure, and the adoption of smart appliances, have changed distribution networks from passive to active. The next-generation of DNs should be efficient and optimized system-wide, highly reliable and robust, and capable of effectively managing highly-penetrated PEVs, RES and other controllable loads. To meet new challenges, the next-generation DNs need active distribution management (ADM). In this thesis, we study the management of PEVs and RES in active DNs. First, we propose a novel discrete-event modeling method to model PEVs and other loads in distribution networks. In addition, a new optimization algorithm to integrate as many PEVs as possible in DNs without causing voltage issues, including the violation of voltage security ranges and voltage stability, is studied. To further explore the active management of PEVs in the DNs, we develop a universal demonstration platform, consisting of software packages and hardware remote terminal units. The demonstration platform is designed with the capabilities of measurement, monitoring, control, automation, and communications. Furthermore, we have studied the reactive power management in microgrids, a special platform to integrate distributed generations and energy storage in DNs. To solve possible voltage security issues in a microgrid with high penetration of single-phase induction machines under the condition of fault-induced islanding, a voltage-sensitivity-based reactive power management algorithm is proposed