Integration of Utility Distributed Energy Resource Management System and Aggregators for Evolving Distribution System Operators

With the rapid integration of distributed energy resources(DERs),distribution utilities are faced with new and unprecedented issues.New challenges introduced by high penetra-tion of DERs range from poor observability to overload and reverse power flow problems,under-/over-voltages,maloperation of legacy protection systems,and requirements for new planning procedures.Distribution utility personnel are not adequately trained,and legacy control centers are not properly equipped to cope with these issues.Fortunately,distribution energy resource management systems(DERMSs)are emerging software technologies aimed to provide distribution system operators(DSOs)with a specialized set of tools to enable them to overcome the issues caused by DERs and to maximize the benefits of the presence of high penetration of these novel resources.However,as DERMS technology is still emerging,its definition is vague and can refer to very different levels of software hierarchies,spanning from decentralized virtual power plants to DER aggregators and fully centralized enterprise systems(called utility DERMS).Although they are all frequently simply called DERIMS,these software technologies have different sets of tools and aim to provide different services to different stakeholders.This paper explores how these different software technologies can complement each other,and how they can provide significant benefits to DSOs in enabling them to successfully manage evolving distribution networks with high penetration of DERs when they are integrated together into the control centers of distribution utilities.

Decentralized coordination of distributed energy resources through local energy markets and deep reinforcement learning

As the energy landscape evolves towards sustainability,the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid.One significant aspect of this issue is the notable increase in net load variability at the grid edge.Transactive energy,implemented through local energy markets,has recently garnered attention as a promising solution to address the grid challenges in the form of decentralized,indirect demand response on a community level.Model-free control approaches,such as deep reinforcement learning(DRL),show promise for the decentralized automation of participation within this context.Existing studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics,overlooking the crucial goal of reducing community-level net load variability.This study addresses this gap by training a set of deep reinforcement learning agents to automate end-user participation in an economy-driven,autonomous local energy market(ALEX).In this setting,agents do not share information and only prioritize individual bill optimization.The study unveils a clear correlation between bill reduction and reduced net load variability.The impact on net load variability is assessed over various time horizons using metrics such as ramping rate,daily and monthly load factor,as well as daily average and total peak export and import on an open-source dataset.To examine the performance of the proposed DRL method,its agents are benchmarked against a optimal near-dynamic programming method,using a no-control scenario as the baseline.The dynamic programming benchmark reduces average daily import,export,and peak demand by 22.05%,83.92%,and 24.09%,respectively.The RL agents demonstrate comparable or superior performance,with improvements of 21.93%,84.46%,and 27.02%on these metrics.This demonstrates that DRL can be effectively employed for such tasks,as they are inherently scalable with near-optimal performance in decentralized grid management.