Preparing for Distributed Energy Resources

Transcription

1 Preparing for Distributed Energy Resources Executive summary Many utilities are turning to Smart Grid solutions such as distributed energy resources (DERs) small-scale renewable energy sources and energy storage to balance load and capacity without building large-scale generation. This paper explains how successful DER implementation depends upon an Advanced Distribution Management System (ADMS) that enables real-time, software-based network modeling to accurately forecast, monitor, control, and analyze the contribution of distributed energy to the grid AR0

2 Summary Executive Summary... p 1 Introduction... p 2 The state of the Smart Grid... p 4 What s needed to move Smart Grid implementations ahead?... p 5 Distributed energy resources are becoming a new normal... p 6 Load transfer with distributed generation... p 7 Getting ready with software... p 8 ADMS optimizes DER management; grid operations and planning... p 9 Conclusion... p 11

3 Executive summary More and more, as utilities face decreasing margin between system load and system capacity, they are in need of innovative smart grid solutions that can help them effectively disperse and store energy and manage load to meet resource requirements. Many are incorporating Distributed Energy Resources (DERs) to help fill the gap while, at the same time, meet requirements for reduced emissions and energy independence; these utilities will require the capability to accurately forecast and control DER contribution to the network, to assure security and grid reliability. Advanced smart grid software designed to support DER management and optimize grid operations and planning works with a real-time network model, based on an accurate geodatabase and incorporating data from operational systems such as a supervisory control and data acquisition (SCADA) system and outage management system (OMS). Along with real-time visualization and monitoring of network status, this Advanced Distribution Management System ADMS provides a host of analytical tools that recommend the most optimal device operations, or optionally automate device operations, to maximize network efficiency and reliability. For example, the utility can apply Volt/VAR control to reduce feeder voltage automatically with no effect on the consumer. Detailed load profiling and load forecasting based on integrated weather feeds yield network load forecasting for effective renewables integration. Network simulation helps forecast medium-term and long-term load and supports effective development and planning. ADMS functionality and tools are demonstrating that utilities can effectively manage demand without building large-scale generation. White paper 01

4 Introduction The deployment of Distributed Energy Resources (DERs) is growing as is the impact on electric utility distribution networks. While DERs are increasing renewable energy with their multitude of benefits, there are many concerns utilities must tackle to assure successful management of a diverse and distributed energy mix. Here, we discuss how DERs will contribute to achieving a smart electric grid and how proper network planning, monitoring, analysis, and control, through ADMS, can transform distributed generation into an efficient asset. White paper 02

5 Preparing for Distributed Energy Resources

6 The state of the Smart Grid Up to now Energy policies are evolving worldwide, with differing regulations from country to country and within countries, helping drive Smart Grid investment priorities. While the impetus to embark on advanced Smart Grid initiatives varies, and no two projects are the same, there are some common drivers: Regulations promoting reduced carbon emissions, renewable technology and energy independence - Different locations have more advanced regulations than others - Wind and solar have become viable energy sources - Storage is enabling permanent and on-demand load shifting Stimulus funding helping advance deployment of advanced technology Convergence of traditional generation capacity and increasing system load Near term Regional factors are likely to predominate: Terminology Here s how we are using some common industry terms Distributed Energy Resources (DERs) small-scale power generation technology that supplies less than 10 MW and is located throughout the distribution network. Increasingly, DERs consist of renewable energy and energy storage making DERs a popular component of Smart Grid implementations. Distributed Generation (DG) referring to any dispersed generation less than 100 MW. In this paper we are considering DG as a smallerscale, subset of DER Electric Vehicles (EV) serve as a source of significant load but can also serve as a form of virtual generation (storage) Demand Response (DR) management of consumption, anywhere along a feeder, in response to supply conditions Microgrid a local network of DERs that is a subset of the distribution network. It can operate in an isolated manner or be connected. Microgrid management targets local energy supply and demand. Electric vehicles (EVs) will begin to make an impact on the distribution system. Their initial effect is not expected to be system-wide; instead, early adopters are likely to be localized within specific areas of a utility s service territory, impacting the network at the distribution transformer level. See sidebar discussion for more about planning for the deployment of EVs. As volatile renewables those such as solar and wind that are intermittent sources see increased deployment, Information Technology (IT) solutions will be integral to their success and storage technology will have to advance. Policy concerns Customer privacy and cyber security will continue to provide some challenge to Smart Grid implementation. White paper 04

7 What s needed to move Smart Grid implementations ahead? Many utilities are experiencing a common trend: the margin between system load and system capacity is decreasing and is expected to continue to decrease. The utility can incorporate new power sources, purchased or generated; improve demand management; and add storage capability in order to maintain a healthy margin between load and capacity. Management of demand is the option least utilized, yet it poses significant potential because of the several innovative ways it can be implemented. Need to fill the gap The Ontario Power Authority of Canada collected data identifying the existing power sources that have been meeting its resource requirements over the past few years and its forecast of available generation in the coming years; see Figure 1. This report forecasts retirement of most existing nuclear facilities, a decrease in reliance on existing oil and gas and coal sources and continuation of existing renewables. What power sources, including virtual sources, are to be added to meet the increasing annual peak forecasts? What is going to fill the gap? Many utilities will be required to disperse and store energy and manage load to meet resource requirements. Electric vehicles Utility thought leaders concur that penetration of EVs will initially be concentrated in localized areas (early adopter neighborhoods) impacting secondary networks of the distribution system. Nevertheless, penetration of EVs will require planning: Battery-charging scenarios vary: the higher the charging level, the faster the charge and the greater the energy demand. Permitting processes should be defined to identify where EVs will reside. Rate structures are needed to help control charging. Real-time monitoring can help model demand accurately. Network planning can preemptively address potential issues. Promotion by the utility can help encourage desired charging habits. The utility armed with mitigation strategies will be best prepared to meet the demand and supply challenges, and the environmental and commercial benefits, of EVs. Figure 1. Expected change in how existing power sources contribute toward resource requirements (effective MW). Source: Ontario Power Authority White paper 05

8 Distributed energy resources are becoming a new normal Regulatory driver While some utility customers are installing renewable generation on their own initiative, the primary drive is coming from regulations that push for reduced emissions and energy independence. For example The Canadian province of Ontario has implemented an aggressive feed-in tariff (FIT) that supports penetration of DER ( California s general strategy of cutting GHG emissions and creating green jobs includes these 2020 targets: 33 percent of energy sourced from renewables; installation of one million solar rooftops; and stimulation of EV deployment and battery storage implementation ( New business model Utilities incorporating DER will have to plan for new connections and ways to achieve accurate forecasting and the control needed for grid reliability and security. For these utilities, distributed energy resources will become a major factor in the new utility business model; see Figure 2. At the heart of the new model is the centralized intelligence system that integrates and manages devices, with intelligence moving out to provide more comprehensive management and collect more data. Figure 2. Distributing energy resources is expected to be the new paradigm in utility management. Source: Progress Energy. White paper 06

9 Load transfer with distributed generation Figure 3 illustrates, very simply, the existence of DG in the event of a feeder trip. The DG, along with the neighboring feeder, might help back-feed the feeder in question. Real-time data and accurate network representation are needed to facilitate the response decisions required for safe and reliable transfer of load. The presence of DG will benefit from adaptive relay protection to properly deal with the initial fault and manage increasing load following restoration. Figure 3. Managing load with distributed generation. White paper 07

10 Getting ready with software The next logical question: how do utilities manage load to maintain the margin between load and system capacity and plan for and leverage DER, to meet increasing demand? Operating the electric distribution network with a growing number of distributed energy devices (DERs) is simply not feasible without the deployment of advanced software analytics specifically, a real-time network model that will support operations management, network optimization and comprehensive planning. This model resides at the centralized control center illustrated in Figure 2 and is created and maintained by advanced Smart Grid software. With this software, utilities can integrate DER to defer capital expenditures for new generation sources; see Figure 4. Figure 4. Distribution network load is expected to continue to increase, in large part due to population growth and the proliferation of consumer technology. A smart IT control system enables network management that will, in effect, increase system capacity and maintain the margin between load and capacity without investment in new and costly traditional generation facilities. White paper 08

11 ADMS optimizes DER management; grid operations and planning ADMS is the large-scale IT control system that can serve as the brain of the distribution network and support network operating decisions. It leverages the GIS as-built network model and integrates with many operational systems such as supervisory control and data acquisition (SCADA) systems and outage management systems (OMS), to create a real-time network model; see Figure 5. Utilities minimize losses and maximize reliability and safety by applying ADMS functionality to manage the distribution network throughout the service territory in a real-time manner. The ideal ADMS approach offers three operating approaches to best meet reliability and efficiency goals: Provide users with the solution s advanced tools and visual context Prompt users with recommended switching operations Fully automate network management with closed loop control functionality The ADMS model delivers the information needed across the utility enterprise for: Monitoring, analysis and control of network operations Managing load and adjusting the shape of the demand curve Figure 5. ADMS model provides network visualization via geographic, schematic and station one-line views. Planning analysis: online to evaluate what if scenarios and offline to assess historical activity and plan for future network enhancements Preparing for effective and secure deployment of DER, including storage and microgrids An ADMS solution can deliver a host of analytical functions some of which are identified below that will optimize grid efficiency and enable effective and efficient integration of DERs. Network operation control including Fault Location, Isolation and Service Restoration (FLISR) with optional closed loop control (automated) switching, as well as large area restoration and load shedding to help sustain system stability during extreme peak periods. White paper 9

12 Network operation optimization including Volt/VAR Control to manage load tap changers, capacitors, and voltage regulators with optional closed loop control in a self-healing manner. An ADMS also enables monitoring of renewable energy through detailed load profiling and, with integrated, real-time weather data, supports improved near-term and short-term load forecasting. It also supports thermal energy storage and evolving battery technology. Network operation analysis including energy losses, both technical and commercial; relay protection through settings and device coordination; reporting of harmonic distortion; and contingency and security assessment to identify re-supply options following faults. Network planning including simulation that supports development; minimizing loss and detecting overload for network reinforcement; medium-term and long-term load forecasting; and load growth analysis. Cutting-edge projects are putting demand management to work Automate peak load shaving. Using ADMS Volt/VAR Control functionality, one utility is reducing feeder voltage automatically, with no effect on the consumer, and deferring, or eliminating, the need to build large-scale generation. The ADMS model is helping the utility plan for green MWs. According to a utility spokesperson, We see this project as something that could change the power industry. Maximize Distributed Generation. This utility serves a large, primarily rural territory and looks to support feed-in tariff regulations and distributed renewable energy. It is deploying ADMS modeling functionality to monitor the high growth of DG and proactively plan for effective dispatch and control of DGs. The utility is doing this in a way that also provides economic benefits, by leveraging network load forecasting based on meter load profiles and integrated weather data. Optimize network efficiency and reliability. The most common benefit utilities realize with ADMS deployment is enabling efficient and reliable network operations in the face of ever-growing constraints. A utility is deploying ADMS to manage its distribution network in a real-time manner to minimize losses and maximize reliability and safety. ADMS provides three operational approaches this utility can use for device management: availability of advanced tools and visual context; recommendation of the most optimal device operations; and automation of device operations. White paper 10

13 Conclusion Security is of utmost importance when deploying ADMS in a mission-critical environment. From a standards perspective, much work remains to be done to address Smart Grid cyber security. There is significant benefit in developing ADMS technology that addresses the evolving NERC CIP and NISTIR requirements. One of the best ways to address security concerns is deploying a single solution that integrates ADMS with SCADA technology with a proven, high-level of security, reliability and performance. Of course, the SCADA incorporated in this solution must: OASyS DNA security Telvent collaborates with Idaho National Laboratories (INL), the host of the United States Department of Energy s National SCADA test bed, in joint cyber security testing of Telvent Energy s OASyS DNA SCADA infrastructure and in developing and documenting best practices. Be able to support tens of thousands of intelligent field devices Have a robust reporting engine to deliver real-time data for critical business and operational analysis and decisions Support a self-healing network architecture Perform system-wide health monitoring Be designed for standards compliance that will support long-term deployment A comprehensive ADMS solution applies this combined-technology approach. It creates a single infrastructure and user interface for enterprise consistency and efficiency. With its comprehensive set of tools, utilities can perform monitoring, analysis, control, dispatch, planning and training for their distribution networks, using real-time, planning, or study modes. The most-advanced technology supports both three-phase balanced and unbalanced state estimation. With it, the utility can take advantage of advanced load management (DSDR), closed-loop control for self-healing automation, and distributed energy resource modeling that supports economic decisions and reliability management. White paper 11

14 Schneider Electric USA, Inc Royal Vista Circle Fort Collins, CO Phone: (34) Fax: June Schneider Electric. All rights reserved.