Disruption is a prevailing and uncompromising theme of technological innovation in the 21st century. Technologies that can slice through preexisting layers of regulatory and business model processes to directly connect customers to the goods and services they seek are gaining traction across global markets. As a result, the regulatory compacts and constructs that have governed various forms of natural monopolies in energy markets face formidable challenges from distributed energy resources (DER). Chief among these challenges is a growing demand from customers to integrate these cutting-edge products to the electric distribution grid.
From solar photovoltaics (PV) and energy storage to demand response (DR)-enabled thermostats and electric vehicles (EVs), technologies with capabilities that were previously only provided to electric grids from supply-side resources are becoming available to the average consumer. Navigant Research expects the installed cost for PV in global markets to fall below $1.50 per watt by 2024, while the North American market for EV services and equipment is expected to grow to more than $4 billion by 2025. These trends fueling DER adoption bring with them complications for network operators, requiring nuanced coordination. This tangible convergence of DER interconnections to networks ill-suited to integrate demand-side behaviors is ground zero for DER disruption of the global energy landscape. The answer? Integrated Distributed Energy Resources (iDER) portfolios – and they’re coming.
As visionary policymakers and market innovators drive disruptive DER through public and private investments, existing frameworks for calculating and conceptualizing the value streams DER offer are limited in scope and scale. But there is a new vision for maximizing the value of DER coming into focus. It is a custom, portfolio-based, DER adoption approach that can work at every level of energy markets. From utility procurement departments to corporate building fleets to single family homes, valuing DER in portfolios offers tremendous opportunities to maximize both return on investment and societal benefits.
The concept of integrated DER (iDER) portfolios started out as “virtual power plants.” In that iteration, the focus was on the DR or generation potential of assets alone. But now the full suite of ancillary services that these aggregated resources represent is gaining recognition.
The potential for iDER portfolios varies across jurisdictions, market environments, and transmission and distribution network structures, but in the best case scenario distribution networks will act as plug-and-play platforms, as is envisioned by New York’s Public Service Commission in its “Reforming the Energy Vision” proceeding. Market actors (such as iDER aggregators, utilities, and independent power producers) who seek to make use of these platforms—aggregating iDER value streams from wholesale markets, energy procurement processes, prosumer power preferences, and regulatory incentive programs through portfolio-based approaches—are poised to lead energy markets into a fruitful distributed future. Much of the necessary change is dependent on the capability of the network system to respond to change and adapt to varying conditions in the grid on a real-time basis. For this reason, distributed intelligence, sensing, and other advanced utility technologies become leverage points for an innovative iDER portfolio.
Navigant believes that a successful operations oriented iDER strategy will incorporate technologies such as machine learning, smart devices (e.g., thermostats and EVs) and the latest generation of advanced metering infrastructure (AMI) systems that incorporate embedded distributed intelligence capabilities. For example, the development of iDER requires a broad spectrum of engineering needs, including everything from smart interconnection and DER integration capability in terms of balancing, to enabling substations and customer devices for participation in coordinated distribution and regional electrical system architecture.
Interest in developing these capabilities is not new. An overarching goal put forth by the U.S. Department of Energy in 2008 to develop “intelligent, efficient, quality-focused and environment friendly” architecture requires the use of information across the system, predicated on carefully managed communications infrastructure and measurement systems to inform control, develop analytics, and support more automated decision-making. As a result, Navigant envisions machine learning and computational intelligence to have an important role in the interoperability, operation, and management of the grid, and system operations will increasingly become automated through the use of coordinated intelligent electronic devices.
Navigant recommends the following two technologies be considered at a minimum when developing a comprehensive iDER operations strategy:
- Machine Learning: Currently, operators rely on conventional techniques such as time series analysis, regression analysis, and other statistical approaches to improve the forecasting and modeling of power system resources. Machine learning advances application of these techniques and incorporates the development of learning algorithms and simulations that may improve prediction and decision-making based on the availability of real-time performance data. Advanced outage management and restoration, productive asset maintenance, power quality monitoring and management, automated asset monitoring, and revenue protection have all been demonstrated to benefit from the application of computational intelligence. Relevant technologies include but are not limited to the development of frameworks and techniques supporting expert systems and legacy applications, dynamic programming, agent-based simulation, and adaptive artificial neural networks.
- Integrated AMI Networks: Advanced meters available today employ the equivalent computing power of a smartphone, many with onboard computing and sensing capability. The latest generation of AMI networks also incorporates support for DER management and distributed intelligence at the grid edge. These networks may be used to support the communication requirements for dynamic interaction between distributed generation, EVs, home energy management systems, and smart building control systems. AMI systems may also serve to support new services, such as distributed decision-making for DER optimization, grid edge decision support for outage and fault reporting, data storage, and networking services between end devices and cloud-based computing assets. For example, AMI networks can be configured to support interactive device communication behind the meter, using multiple communication media formats, and support complex calculations by calling on demand reporting and processing capabilities.
iDER, when combined with distributed intelligent systems, promises the improvement of reliability and efficiency of the grid, and with the development and application of machine learning techniques applied across distribution systems, it will enhance the potential for improved operations and greater customer value at every point in the electric power system.