The electric power grid is undergoing a transformation as we seek to reduce our environmental footprint and reliance on foreign fuels while simultaneously electrifying transportation and heating. These concerns have led to a focused effort towards a high penetration of distributed energy resources (DERs), which include distributed generation (DG), demand response (DR), and storage. These small-scale resources can provide various services to the grid including voltage support from clusters of DERs, reduced line congestion from better generation/load management, lower operating costs by using cheaper resources (ex. renewables), and demand flexibility by enabling DR throughout the distribution grid. Technologies such as storage and price-responsive demand systems are also being adopted at an accelerating pace, in an attempt to reduce operational costs and manage increasingly dynamic electrical system conditions. Optimization and control methods need to be revisited to efficiently integrate these new technologies, transforming our energy systems into smart grids.
Retail Electricity Markets for Distribution Grids
In the United States, the procurement and integration of distributed generators (DGs) is largely limited to providing ancillary services through participation in the Wholesale Electricity Market (WEM); there is some participation from DR units and storage devices evidenced by FERC order 841. Typically, these DERs must meet minimum size requirements, with some electricity markets not allowing aggregation. As the penetration of DERs increases, specifically renewable generation, demand response, and storage, the WEM alone may not suffice in realizing an efficient and reliable power delivery. A properly designed retail market that oversees the participation of variable scale DERs in the distribution grid and implements a suitable mechanism for their scheduling and compensation is highly necessary.
To address these issues, we have been developing a retail market mechanism which details a real-time pricing scheme for distribution grids in the presence of high DER penetration, enabled by the recently developed distributed optimization algorithm, the proximal atomic coordination algorithm (PAC). We introduce a Distribution System Operator (DSO), which handles market settlements with the WEM on behalf of the distribution grid, charges agents for their consumption, and compensates flexible consumers and generators. By using the retail market, the distribution grid is more efficiently managed, and smaller DERs are able to participate in the WEM by bidding through the DSO.1
Improved hosting capacity
Hosting capacity (HC) is an indicator of the amount of new or additional load or generation that can be interconnected to the distribution system without triggering system upgrades. HC may be limited by voltage, power quality, reliability, thermal or operational constraints. Our current project is focused on the coordination of DERs including siting of Photovoltaics and their capacities, the siting and sizing of Battery Storage and Heat pumps so that we can improve the hosting capacity while ensuring that the voltage stays within limits. The diameter of the yellow dots corresponds to the % increase in the PV capacities (left plot). The right plot shows the amount of voltage support provided by batteries and storage pumps that enables the increased hosting capacity.
Efficient Ultra-coordinated IoT networks for Grid Resilience
The electricity landscape is undergoing a rapid transformation, especially at the grid edge. With every end-user having just 5 connected devices, the grid infrastructure will consist of 8 billion digital nodes. Many of these nodes are capable of sensing, computing, and communicating, thereby possibly enabling controlling and monitoring disturbed generation and consumption at time-scales and line-scales never envisioned before. We have developed an architecture EUREICA (Efficient Ultra Resilient IoT-Coordinated Assets) that enables the grid edge to be resilient. This is accomplished by leveraging the connectivity of IoT devices together with various grid assets. This in tun create situational awareness to the operator and trustability of the local assets which is measured through a resilience score. Several attack scenarios were created in a distribution grid to evaluate the efficacy of EUREICA, in each of which it was shown that the operator is successful in identifying an attack is occurred and dispatching trustable assets.2
Transactive Control of Electric Railway Systems
Electric railway systems are a major untapped source of demand-side flexibility in electricity networks. Electric trains can both demand power from their traction system for locomotion and inject power back into the electricity network through regenerative braking, virtually enabling them to store electricity in the form of kinetic energy. The power profile of a train along a route is in many cases determined by the conductor based on training and experience, attempting to meet a given schedule with little regard to the varying cost of power along the route.3
We propose an alternative operation methodology that solves the energy cost minimization problem, taking into account the scheduling and operational constraints of the railway system. In addition, we provide a control mechanism to coordinate multiple trains and rail-side distributed energy resources (DERs) tied to the electric railway, which dynamically change the price of electricity along the track, ultimately enabling the operational cost minimization of the system. The proposed transactive control methodology has been tested in numerical simulations of the high-speed Amtrak Acela service which operates along the northeast United States resulting in a 25% reduction in energy costs when compared to a standard trip optimization based on minimum work, and a 75% reduction in energy costs when compared to the current train cost calculated using a field dataset.
Plots show position [km] and velocity [m/s] of a southbound trip on Amtrak Acela between University Park Station in MA and New Haven Station in CT with a stop in Providence Station in RI for (1) a train that minimizes work (blue), (2) a train one dispatched following our method (red), and (3) a field train (yellow).
- R. Haider, D. D’Achiardi, V. Venkataramanan, A. Srivastava, A. Bose, and A.M. Annaswamy, 2021. Reinventing the utility for distributed energy resources: A proposal for retail electricity markets. Advances in Applied Energy, 2, p.100026.
↩︎ - Vineet J. Nair jPriyank Srivastava, Venkatesh Venkataramanan, Partha S. Sarker, Anurag Srivastava, Laurentiu D. Marinovici, Jun Zha, Christopher Irwin, Prateek Mittal, John Williams, Jayant Kumar, H. Vincent Poor and Anuradha M. Annaswamy 2025. Resilience of the electric grid through trustable iot-coordinated assets. Proceedings of the National Academy of Sciences.
↩︎ - D. D’Achiardi, A.M. Annaswamy, S.K. Mazumder, and E. Pilo, 2022. Transactive control of electric railways using dynamic market mechanisms. IEEE Transactions on Control Systems Technology, 31(2), pp.748–760. ↩︎