Smart Grid

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 drive the plummeting costs and increasing adoption of Renewable Energy Resources (RERs) to replace aging generators across the electrical grid. The inherently intermittent nature of these resources requires a new approach in ensuring the security of supply and reliability of the transmission and distribution systems. 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. Sensing and control systems need to be revisited to efficiently integrate these new technologies, transforming our energy systems into smart grids.

The Active Adaptive Control Laboratory at MIT has been working on identifying and addressing some of these crucial challenges for smart grid modeling and control, and are delineated below. The following publications outline a number of these challenges:
  • J. Stoustrup, A. Annaswamy, A. Chakrabortty, and Z. Qu (eds.), "Smart Grid Control: An Overview and Research Opportunities". Springer, 2019.
  • A.M. Annaswamy (Project Lead), "Vision for Smart Grid Control: 2030 and Beyond", Eds: M. Amin, A.M. Annaswamy, C. DeMarco, and T. Samad, IEEE Standards Publication, June 2013.
The following are specific topics that are currently under investigation:


Distributed Optimization Algorithm for Smart Distribution Grids

Convergence Results for PAC The modern grid is characterized by the high penetration of Distributed Energy Resources (DERs), including generation units, flexible consumption units (demand response), and storage, which are largely owned by different third-party agents. To take advantage of these distributed resources and maintain privacy for individual agents, a fully distributed control architecture is necessary. This corresponds to having a fully distributed optimization algorithm, which will solve the Optimal Power Flow (OPF) problem: determining actuation of DERs constrained by power flow physics, grid limits, and meeting electricity demand.

Atomization Example for PAC

This project has culminated in the fully distributed Proximal Atomic Coordination (PAC) algorithm, which exhibits similar convergence rates to the popular Alternating Direction Method of Multipliers (ADMM) algorithm (O(1/tau), tau = number of iterations), but has lower communication requirements, shorter iteration time, and most crucially, data privacy between agents is an intrinsic property of PAC.

An optimization problem over a connected network is decomposed into K different coupled sub-optimization problems. Dependencies between atoms are dealt with by creating local “copies” of variables owned by other atoms, and introducing additional constraints to force these local copies to converge to the real values of the variables. In contrast, ADMM treats dependencies as shared variables between atoms. The dynamics of the PAC algorithm involve the system moving towards feasibility (meeting OPF constraints) and moving towards consistency (local copies = real variables), while trying to minimize a Lagrangian objective function for each agent.

PAC Algorithm Dynamics

Future works:
  • Analyze robustness of PAC to cyber-events (failure or attack in communication network)
  • Develop asynchronous PAC variant

Retail Market Mechanisms for Distribution Grids

Price Demand Curve showing Inefficiencies

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.

High-level approach for Retail Markets

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. A case study conducted on a distribution grid in Komae City, Tokyo, Japan, shows the retail market mechanism results in projected savings over a 24-hour period for the DSO.

Retail Market Mechanism Future works:
  • Relaxing assumption that the load/generation profile of DSO is not binding at each WEM clearing
  • Designing a bidding mechanism between the DSO and WEM
  • Multi-period market settlements
  • Incorporating storage units and appropriate compensation
  • Better modeling of DR contracts

Controllability and Observability in Power Distribution Systems

DERs present a golden opportunity to improve the resilience of the distribution system. This necessitates an intelligent management system previously employed only at the transmission level. The goal is a Distribution Management System (DMS) that will optimally utilize DERs to improve outage prevention and management. The DMS will rely on metrics for controllability and observability, dual concepts from control theory that serve as vital signs of sorts for the network. Our ongoing project in the AAC laboratory concerns the development of these metrics that adapt the definition of controllability and observability to the quasi-static distribution system. Supported by the US DOE Office of Electricity Delivery and Energy Reliability, our research investigations are focused on the development of a new framework for DER-based distribution system management. Highlights of our research are given below:

The notion of observability is a measure of how well internal states of a system can be reconstructed using a given set of measurements. In order to develop the observability analysis component of DMS, we have extended a transmission system observability test from the literature to the distribution system.The original algorithm was based on the canonical linear decoupled power flow model which is ill-suited to the distribution system where line resistance to reactance ratios approach unity.Instead, the test has been modified to accommodate the three phase linear coupled power flow model that was developed recently for the distribution system.To accompany the new distribution system observability test, we have developed an observability metric, based on the smallest eigenvalue of the gain matrix, that captures the visibility of the network based on the given measurements. Work is ongoing to exploit correlation between loads and DERs to improve observability. Techniques from parameter estimation theory may be leveraged for this problem.

The notion of controllability is a measure of how well internal states of a system can be controlled using a given set of inputs or actuators. In this case, the actuators are DERs as well as conventional control mechanisms such as tap changers and capacitor banks. A controllability metric is currently under development based on the voltage-current controllability index (VICI) from the literature.


Transactive Control of Electric Railway Systems

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.

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 suggesting savings on the order of 10% of the annual energy costs of the operator.


Interdependent Natural Gas and Electricity Infrastructures

One of the fastest growing consumers of Natural Gas (NG) is the electricity sector, through the use of NG-fired power plants. With increased renewable energy generation, natural gas power plants have become key players due to their relatively low fuel costs and ability to ramp in response to changes in wind and solar resources. As a result, NG and electricity networks are increasingly linked and interdependent. Our lab has led research projects examining this interdependency, developing tools to improve the operation of these infrastructures. Two such tools are described in more detail below:

Reliability Contracts Between Renewable and Natural Gas Power Producers

Reliability Contracts Between Renewable and Natural Gas Power Producers

Renewable power adoption has required policies that protect intermittent generators, such as wind and solar, from system-level costs of resource shortfalls. It has been shown that if renewable generators were to accommodate these costs in energy market settlement, significant renewable generation curtailments would ensue, especially as the penetration of renewables grows. Based on the current evolution of policies towards unmet commitment penalties for intermittent generators, we developed a reliability contract between a renewable power producer (RPP) and a natural gas power plant (NGPP) where the NGPP fulfills the RPP unmet commitments in low resource scenarios. We analyze the contract against a baseline scenario where the RPP faces the energy market shortfall penalty, deriving optimal commitments and a condition where the adoption of the reliability contract increases social welfare. Using real data from a RPP-NGPP pair in Northeastern United States, the contract is shown to improve renewable utilization, increase the profits of both partners, and decrease total unmet commitments through the introduction of a lower-cost alternative to the shortfall penalty.

Modeling Renewable Generation Impacts on Natural Gas Markets

Computational tools, in the form of price regression models and market auction simulations, were developed to evaluate the impact of increased renewable adoption in New England’s coupled electric-natural gas system. Natural gas infrastructure parameters such as pipeline capacity were compared to forecasted requirement values for scenarios across varying levels of renewables, climate change impact and macroeconomic indicators. Both physical and economic system domains were studied, with a focus on developing recommendations to more efficiently manage the joint electricity-natural gas system and accommodate the growth of renewable generation. One of the main results from our simulation work is that increasing renewable adoption can increase natural gas market prices due to the increased variance in demand for natural gas demand, product of the increasing variance in renewable power output.


Dynamic Market Mechanisms for Wholesale Energy and Regulation Markets

Transactive energy control Renewable energy resources (RER) generate intermittent power which, along with fast variations of the electricity demand, cause power systems conditions to vary more frequently. This necessitates dynamic tools for analysis and control of power system operations and for market design, that can align the timescales of decision making and control processes with the natural timescales in which power systems’ conditions evolve. For this purpose, a series of dynamic market mechanisms (DMM) have been developed in the past decade in our group and are currently under development. These DMM realize dynamic solutions to different variations of optimal power flow problems in a market setting where, instead of solving the optimization problem at a single instant based on predicted conditions for a long horizon, they continuously utilize more frequently updated information about the systems’ conditions as it becomes available, providing new optimal solutions to these problems that correspond to the updated conditions. The DMM approach has been extended in many directions with its initial versions realizing solutions to optimal power flow problems for real-time wholesale markets to more recently realizing solutions to the optimal frequency regulation problem. DMM enables optimal and dynamic scheduling and utilization of resources in the fast power systems timescales, leading to improvements in economic efficiency and the efficient integration of renewable power.

Our recent work has addressed the following topics:
  • Stability of DMM
  • A Hierarchical Transactive Control Architecture for Renewables Integration
  • Integration of Demand Response in Electricity Markets
  • A Dynamic Regulation Market Mechanism leading to an optimal AGC and reduced make-whole payments

Co-Design of Wide Area Control and Monitoring of Power Grids

We address the problem of wide-area control of power systems in presence of different classes of network delays. We pose the control objective as an LQR minimization of the electro-mechanical states of the swing equations, and present an arbitration approach by which the flexibilities of the communication network such as scheduling policies, bandwidth, etc., can be exploited to design a delay-aware state feedback control law.

The delay-aware strategy is based on a sparse and distributed optimal control strategy. Sparsity is introduced in the underlying communication network on the basis of dominant participation of the state variables in the inter-area oscillation modes that decides the necessary generation units that need to communicate and included in the control design. In addition, the controller accommodates large network delays that are of values four to five times greater than the sampling period. A virtual sparsity concept is introduced to accommodate these delays by zeroing out the gains that correspond to measurements that are yet to arrive. Results are verified through a simulation study of the IEEE-39 bus power system model, where it is shown that with 86% less communication channels, we can obtain nearly 89% of the performance compared to the case with a non-sparse controller.