Deriving policies from connection codes to ensure ongoing voltage stability

The management of distribution and transmission networks is becoming increasingly complex due to the proliferation of renewables-based distributed energy resources (DER). Existing control systems for DER are based on static specifications from interdependent network connection documents. Such systems are inflexible and their maintenance requires concerted effort between grid stakeholders. In this paper we present a new supplementary control approach to increase the agility of the electricity grid. The ICT system that underlies smart grids has the potential to offer, by analogy with ICT based network management, a control plane overlay for the modern smart grid. Policy-based Network Management (PBNM) is widely deployed in managed telecoms networks. We outline how PBNM can augment the management of power and energy networks and report on our initial work to validate the approach. To configure the PBNM system, we have used text mining to derive connection parameters at the LV level. In our simulations, PBNM was used in collaboration with a Volt-VAr optimisation (VVO) to tune the connection settings at each DER to manage the voltage across all the buses. We argue that the full benefits will be realised when stakeholders focus on agreeing relatively stable high-level connection policies, the policies being refined dynamically, and algorithms such as VVO that set connection parameters so they are consistent with those high-level policies. Thus faults, power quality issues and regulatory infringement can be identified sooner, and power flow can be optimised

Coordinated Voltage and Reactive Power Control of Power Distribution Systems with Distributed Generation

Distribution system voltage and VAR control (VVC) is a technique that combines conservation voltage reduction and reactive power compensation to operate a distribution system at its optimal conditions. Coordinated VVC can provide major economic benefits for distribution utilities. Incorporating distributed generation (DG) to VVC can improve the system efficiency and reliability. The first part of this dissertation introduces a direct optimization formulation for VVC with DG. The control is formulated as a mixed integer non-linear programming (MINLP) problem. The formulation is based on a three-phase power flow with accurate component models. The VVC problem is solved with a state of the art open-source academic solver utilizing an outer approximation algorithm. Applying the approach to several test feeders, including IEEE 13-node and 37-node radial test feeders, with variable load demand and DG generation, validates the proposed control. Incorporating renewable energy can provide major benefits for efficient operation of the distribution systems. However, when the number of renewables increases the system control becomes more complex. Renewable resources, particularly wind and solar, are often highly intermittent. The varying power output can cause significant fluctuations in feeder voltages. Traditional feeder controls are often too slow to react to these fast fluctuations. DG units providing reactive power compensation they can be utilized in supplying voltage support when fluctuations in generation occur. The second part of this dissertation focuses on two new approaches for dual-layer VVC. In these approaches the VVC is divided into two control layers, slow and fast. The slow control obtains optimal voltage profile and set points for the distribution control. The fast control layer is utilized to maintain the optimal voltage profile when the generation or loading suddenly changes. The MINLP based VVC formulation is utilized as the slow control. Both local reactive power control of DG and coordinated quadratic programming (QP) based reactive power control is considered as the fast control approaches. The effectiveness of these approaches is studied with test feeders, utility load data, and fast-varying solar irradiance data. The simulation results indicate that both methods achieve good results for VVC with DG

Power Quality Concerns in Implementing Smart Distribution-Grid Applications

This paper maps the expected and possible adverse consequences for power quality of introducing several smart distribution-grid technologies and applications. The material presented in this paper is the result of discussions in an international CIGRE-CIRED joint working group. The following technologies and applications are discussed: 1) microgrids; 2) advanced voltage control; 3) feeder reconfiguration; and 4) demand-side management. Recommendations are given based on the mapping

Digital Twins and Artificial Intelligence for Applications in Electric Power Distribution Systems

As modern electric power distribution systems (MEPDS) continue to grow in complexity, largely due to the ever-increasing penetration of Distributed Energy Resources (DERs), particularly solar photovoltaics (PVs) at the distribution level, there is a need to facilitate advanced operational and management tasks in the system driven by this complexity, especially in systems with high renewable penetration dependent on complex weather phenomena. Digital twins (DTs), or virtual replicas of the system and its assets, enhanced with AI paradigms can add enormous value to tasks performed by regulators, distribution system operators and energy market analysts, thereby providing cognition to the system. DTs of MEPDS assets and the system can be utilized for real-time and faster-than-real-time operational and management task support, planning studies, scenario analysis, data analytics and other distribution system studies. This study leverages DT and AI to enhance DER integration in an MEPDS as well as operational and management (O&M) tasks and distribution system studies based on a system with high PV penetration. DTs have been used to both estimate and predict the behavior of an existing 1 MW plant in Clemson University by developing asset digital twins of the physical system. Solar irradiance, temperature and wind-speed variations in the area have been modeled using physical weather stations located in and around the Clemson region to develop ten virtual weather stations. Finally, DTs of the system along with virtual and physical weather stations are used to both estimate and predict, in short time intervals, the real-time behavior of potential PV plant installations over the region. Ten virtual PV plants and three hybrid PV plants are studied, for enhanced cognition in the system. These physical, hybrid and virtual PV sources enable situational awareness and situational intelligence of real-time PV production in a distribution system

Intelligent Energy Management for Microgrids with Renewable Energy, Storage Systems, and Electric Vehicles

The evolution of smart grid or smart microgrids represents a significant paradigm shift for future electrical power systems. Recent trends in microgrid systems include the integration of renewable energy sources (RES), energy storage systems (ESS), and plug-in electrical vehicles (PEV or EV). However, these integration trends bring with then new challenges for the design of intelligent control and management system. Traditional generation scheduling paradigms rely on the perfect prediction of future electricity supply and demand. They can no longer apply to a microgrid with intermittent renewable energy sources. To mitigate these problems, a massive and expensive energy storage can be deployed, which also need vast land area and sophisticated control and management. Electrical vehicles can be exploited as the alternative to the large and expensive storage. On the other hand, the use of electrical vehicles introduces new challenges due to their unpredictable presence in the microgrid. Furthermore, the utility and ancillary industries gradually adding sensors and power aware, intelligent functionality to home appliances for the efficient use of energy. Hence, the future smart microgrid stability and challenges are primarily dependent on the electricity consumption patterns of the home appliances, and EVs. Recently, demand side management (DSM) has emerged as a useful method to control or manipulate the user demand for balancing the generation and consumption. Unfortunately, most of the existing DSM systems solve the problem partially either using ESS to store RES energy or RES and ESS to charging and discharging of electrical vehicles. Hence, in this thesis, we propose a centralized energy management system which jointly optimizes the consumption scheduling of electrical vehicles and home appliances to reduce the peak-hour demand and use of energy produced from the RESs. In the proposed system, EVs store energy when generation is high or during off-peak periods, and release it when the demand is high compared to the generation. The centralized system, however, is an offline method and unable to produce a solution for a large-scale microgrid. Further, the real-time implementation of the centralized solution requires continuous change and adjustment of the energy generation as well as load forecast in each time slot. Thereby, we develop a game theoretic mechanism design to analyze and to get an optimal solution for the above problem. In this case, the game increases the social benefit of the whole community and conversely minimizes each household's total electricity price. Our system delivers power to each customer based on their real-time needs; it does not consider pre-planned generation, therefore the energy cost, uncertainty, and instability increase in the production plant. To address these issues, we propose a two-fold decentralized real-time demand side management (RDCDSM) which in the first phase (planning phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flat curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another repeated game with mixed strategy to mitigate the deviation between the immediate real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and energy storage systems and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator better deals with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. Now, it is envisioned that the presence of hundreds of microgrids (forms a microgrid network) in the energy system will gradually change the paradigms of century-old monopolized market into open, unbundled, and competitive market which accepts new supplier and admits marginal costs prices for the electricity. To adapt this new market scenario, we formulate a mathematical model to share power among microgrids in a microgrid network and minimize the overall cost of the electricity which involves nonlinear, nonconvex marginal costs for generation and T&D expenses and losses for transporting electricity from a seller microgrid to a buyer microgrid

Coordinated Optimal Voltage Control in Distribution Networks with Data-Driven Methods

Voltage control is facing significant challenges with the increasing integration of photovoltaic (PV) systems and electric vehicles (EVs) in active distribution networks. This is leading to major transformations of control schemes that require more sophisticated coordination between different voltage regulation devices in different timescales. Except for conventional Volt/Var control (VVC) devices such on-load tap change (OLTC) and capacitor banks (CBs), inverter-based PVs are encouraged to participate in voltage regulation considering their flexible reactive power regulation capability. With the vehicle to grid (V2G) technology and inverter-based interface at charging stations, the charging power of an EV can be also controlled to support voltages. These emerging technologies facilitate the development of two-stage coordinated optimal voltage control schemes. However, these new control schemes pursue a fast response speed with local control strategies in shorter snapshots, which fails to track the optimal solutions for the distribution system operation. The voltage control methods mainly aim to mitigate voltage violations and reduce network power loss, but they seldom focus on satisfying the various requirements of PV and EV customers. This may discourage customer-owned resources from participating in ancillary services such as voltage regulation. Moreover, model-based voltage control methods highly rely on the accurate knowledge of power system models and parameters, which is sometimes difficult to obtain in real-life distribution networks. The goal of this thesis is to propose a data-driven two-stage voltage control framework to fill the research gaps mentioned above, showing what frameworks, models and solution methods can be used in the optimal voltage control of modern active distribution systems to tackle the security and economic challenges posed by high integration of PVs and EVs

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Benchmarking Utility Clean Energy Deployment: 2016

Benchmarking Utility Clean Energy Deployment: 2016 provides a window into how the global transition toward clean energy is playing out in the U.S. electric power sector. Specifically, it reveals the extent to which 30 of the largest U.S. investor-owned electric utility holding companies are increasingly deploying clean energy resources to meet customer needs.Benchmarking these companies provides an opportunity for transparent reporting and analysis of important industry trends. It fills a knowledge gap by offering utilities, regulators, investors, policymakers and other stakeholders consistent and comparable information on which to base their decisions. And it provides perspective on which utilities are best positioned in a shifting policy landscape, including likely implementation of the U.S. EPA's Clean Power Plan aimed at reducing carbon pollution from power plants

Improving the intersect of the power distribution system and the built environment in developing countries

Power distribution systems, specifically where they intersect with the built environment, are highly underemphasised versus generation in power planning. In a time of technology advances and cost declines in distribution automation and related technologies, this is an area of high potential for improving energy efficiency. This is particularly of impact in developing countries where urbanisation is rapidly increasing. Evidence shows that the same missed opportunities and sub-optimal distribution planning techniques are repeatedly found across multiple geographies. In this research, tools were developed to rank these problems and create solutions. These tools were endorsed by power industry executives from three countries. Following this, the tools were applied in a developing corridor near the Thailand-Cambodia border where power density is increasing, in order to develop power system solutions for live infrastructure projects. The solutions include technologies such as distributed generation, microgrids, digital monitoring systems, CCHP units, and power storage. The solutions from the live example were then honed and endorsed in an interview with Thai power sector experts. The final research and tools developed were confirmed capable of producing actionable solutions for planners across the public and private sectors, who focus on power distribution in urbanising, developing counties