Smart Grid

The electric power grid is undergoing a transformation necessitated by the need to reduce energy imports from foreign sources, environmental concerns, and the need to increase energy efficiency in the presence of rapid energy demand growth. These concerns have awakened the interest in using a larger number of Renewable Energy Resources (RERs) to supply demand. The inherently intermittent nature of these resources, and their immanent uncertainty, requires a new approach in ensuring the security of supply and reliability of the transmission and distribution systems. We have been developing Dynamic Market Mechanisms (DMM) that enable the overall electricity infrastructure to ensure an efficient economic dispatch and grid stability and reliability. These DMMs are demonstrated to accommodate and leverage a number of enabling technologies including Dynamic Response which is finding increased acceptance, sensing and actuation including AMI, PMU, FACTS, OLTCs, and smart inverters, increasing penetration of EVs, and most importantly distributed and renewable energy resources such as solar and wind based power generation. The DMMs are not only shown to ensure grid performance such as reduced ACE and improved AGC but also efficient market design and improved financial settlements.

Active Adaptive Control Laboratory at MIT has been working on identifying and addressing the crucial challenges for smart grid modeling and control. The following publications outline a number of these challenges:
  • 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.
  • M. Kezunovic, A.M. Annaswamy, I. Dobson, S. Grijalva, D. Kirschen, J. Mitra, and L. Xie, "ENERGY CYBER-PHISICAL SYSTEMS: Research Challenges and Opportunities", NSF Report, August 2014.
The following are specific topics that are currently under investigation:

Some of our recent publications can be found here.

Distributed Optimization Algorithm for Smart Distribution Grids


Interdependent Natural Gas and Electricity Infrastructures

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. Already a large portion of the electricity portfolio mix in many regions in the US, NG-fired power generation is increasing even further with growing penetration of renewable energy due to the former’s fast, on-demand response capabilities, and latter’s characteristics of intermittency and uncertainty. As a result, NG and electricity networks are getting increasingly linked and interdependent. Tighter coordination and information sharing between electric grid operators and NG suppliers is therefore becoming highly necessary to have a resilient interdependent critical infrastructure (ICI) of electricity and NG. This project seeks to create computational models of this ICI, gain analytical insight into the existing ICI in response to system and policy changes, and design guidelines for a coordinated ICI with improved resilience.

  • D.J. Shiltz, M. Cvetkovic, A.M. Annaswamy, “An Integrated Dynamic Market Mechanism for Real-time Markets and Frequency Regulation”, IEEE Transactions on Sustainable Energy, Special Section: Reserve and Flexibility for Handling Variability and Uncertainty of Renewable Generation, April 2015, submitted.
  • N. Nandakumar, A.M. Annaswamy and M. Cvetkovic, “Natural Gas-Electricity Market Design Utilizing Contract Theory”, IEEE PES General Meeting, Poster session, Denver CO, July 2015.
  • M. Cvetkovic, A.M. Annaswamy, “Coupled ISO-NE Real-time Energy and Regulation Markets for Reliability With Natural Gas”, IEEE PES General Meeting, Denver CO, July 2015.
  • S. Jenkins and A.M. Annaswamy, “A Dynamic Model of the Combined Electricity and Natural Gas Markets”, IEEE PES Conference on Innovative Smart Grid Technologies, Washington, DC, February 2015.
  • C. Adcock, M. Cvetkovic and A.M. Annaswamy, "Influence of Natural Gas Price and Other Factors on ISO-NE Electricity Price", Technical report for MIT Undergraduate Research Opportunities Project, February 2015.

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 and more dynamically. This necessitates dynamic tools for analysis and control of power systems 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 and given predicted conditions for a long horizon, they continuously utilize more frequent and updated information about the systems’ conditions that 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. Our group envisions that, DMM, by enabling optimal and dynamic scheduling and utilization of resources in the fast power systems timescales, can lead to high economic efficiency and promote that way, efficient integration of high amounts of renewable power.

The following are some of the topics currently being investigated:

Perturbation Analysis in the Presence of Renewables

Beginning with an overall model of the major market participants together with the constraints of transmission and generation, and its equilibrium under nominal conditions, the effect of uncertainties in the RER on the market equilibrium is quantified, with and without real-time pricing. Perturbation analysis methods are used to compare the equilibria in the nominal and perturbed markets. These markets are also analyzed using a game-theoretic point of view. The perturbed market is analyzed using the concept of closeness of two strategic games and the equilibria of close games.

Stability of Dynamic Market Mechanisms

A dynamic model of the wholesale energy market that captures the effect of uncertainties of renewable energy sources and real-time pricing with demand response is derived, by starting from a standard market optimization problem and using the concept of gradient play. The underlying optimization framework together with a recursive gradient-based approach enables the model to capture dynamic interactions between generation, demand, locational marginal price, and congestion price near the equilibrium of the optimal dispatch. Stability under nominal conditions and robust stability under perturbations due to renewable energy sources and demand response are analyzed.

A Hierarchical Transactive Control Architecture for Renewables Integration

In general, grid-wise information is available at multiple time-scales and from multiple sources, the underlying decision and control algorithms which necessitates the introduction of a hierarchical structure. We have developed a transactive control architecture that combines market transactions at the higher, tertiary, level with inter-area dynamics addressed at a secondary level and unit-level control at the primary level. With a goal of ensuring frequency regulation using optimal allocation of resources in the presence of uncertainties in renewables and load, a hierarchical control methodology is presented. Global asymptotic stability of the overall system is established in the presence of uncertainties at all three timescales and numerically evaluated.

Modeling and Integration of Demand Response in Electricity Markets

In recent decades, moves toward higher integration of Renewable Energy Resources have called for fundamental changes in both the planning and operation of the overall power grid. One such change is the incorporation of Demand Response (DR), the process by which consumers can adjust their demand in a flexible manner. Both locational marginal prices and the schedules for generation and consumption are determined through a negotiation process between the key market players which converges to the desired market equilibrium. In addition to incorporating renewables, this mechanism accommodates both consumers with a shiftable Demand Response and an adjustable Demand Response. The overall market mechanism is evaluated in a Day Ahead Market and is shown in a numerical example to result in a reduction of the cost of electricity for the consumer, as well as an increase in the Social Welfare. Additionally, we put together a survey of various aspects of Demand Response (DR) including the different types of participants, as well as the underlying challenges and the overall potential of DR when it comes to large-scale implementations. Benefits of DR as reported in the literature for performance metrics such as frequency control and price control, as well as methods for ensuring privacy are being investigated. A quantitative taxonomy of DR recently proposed in the literature based on the inherent magnitude, run-time, and integral constraints and its integration with economic dispatch is currently being explored.


iTEACH Architecture for Control of Large Scale Distribution Grids

Given the large dimension of nodes in a distribution grid, a distributed energy management system that manages the huge volume of information and makes decisions that optimize global objectives and manages local outcomes will necessarily have to be hierarchical. This hierarchy will not only have to determine a spatial partitioning of various nodes but also a time-scale partitioning of various decision making. A new concept that’s showing a lot of promise is Transactive Control where intelligent agents such as flexible loads and dispatchable generators throughout the network negotiate the economic contracts to determine the price, quantity, location, and time of delivery of electric energy, thus balancing supply and demand in near-real time. The timescales involved in these negotiations (30 seconds to 5 minutes) compared to the timescales of primary controls of the distributed generators (milliseconds) endears Transactive Control to a hierarchical architecture. Transactive Control provides a significant potential for the active coordination of several DERs through suitably designed incentives for DERs to participate in economic transactions, and ensures reliable power delivery. Our lab is currently developing the iTEACH (integrated Transactive, Efficient, Actively Coordinated, Hierarchical) control architecture that will enable high penetration of penetration, increase the grid efficiency and provide a guarantee of system reliability to the end-user. To this end we are deploying a number of tools including Dynamic Market Mechanisms, hierarchical systems theory, optimization methods, nonlinear systems and control, lessons learned from industry experts, national labs, and pilot projects for high levels of renewable integration.

Distributed State Estimation

While the presence of DERs leads to better sustainability, they can also pose stability problems in the system operation. Distributed state estimation is a central tool in alleviating such stability problems. The proliferation of DERs, most notably small installations of wind turbines and photovoltaic receptors, is straining current electric grids in the distribution level. This in turn necessitates an intelligent management system, previously employed only at the transmission level. The expectation is that a successful distribution management system (DMS) will have the ability to maximize the penetration of DERs while minimizing the overloading or congestion of transmission lines, and prevent faults from cascading throughout the system. Our ongoing project in the AAC laboratory concerns the first step in such a DMS pertaining to reliable, real-time state estimation of the network. The main challenge herein, in contrast to the transmission level, is the presence of significantly smaller ratio of measurements to state variables. Supported by the Siemens CKI program under the MIT Energy Initiative (MITEI), our research investigations are focused on the development of an overall framework for distributed state estimation including fundamental concepts and computational tools. Highlights of our research are given below:

Topology Identification in Distribution Networks

The electric grid is divided to high-voltage transmission networks and low-voltage distribution networks. Transmission networks carry the electric power from the major plants to the load centers. Distribution networks then distribute this power to the residential and commercial consumers. Because so many depend on the power flowing through every node of a transmission network, these networks are highly monitored and controlled remotely from a control center. Distribution networks, in contrast, are scarcely monitored and are managed manually by sending crews to reconfigure them. Until now, and despite having outages that sometimes take hours to identify and restore, there has been little demand to change the way distribution networks are operated. A major reason for this is that until now power flow in distribution networks always flowed one way from the substation, the point of connection to the transmission network, to the consumers. With the expected proliferation of distributed energy resources (DER), based mainly on wind and solar, we expect this to change. As more DERs are introduced, faults and subsequent line disconnections will affect not only those downstream but also those upstream. These networks, which traditionally were operated radially, may need to start to be operated with topologies that include loops in order to minimize power losses. All of these necessitate operating distribution networks more like transmission networks. However, the fundamental economics of distribution networks remain, and prevent equipping distribution networks with redundant real-time measuring capabilities covering every node in the network. Recognizing that the full state estimate required for transmission networks may not be required to perform the new tasks required in distribution networks, we ask whether we can add a few more sensors in order to provide just the information needed for these tasks.

Our recent research has focused on the problem of circuit breakers status detection in non-radial distribution networks. We ask whether we can detect the correct status in real time and with high probability, but without equipping every breaker with a sensor and a transmitter. We follow a state estimation approach, but since we do not have sufficient real-time data for state estimation, we complete the missing information using historical data. Because predicting the present conditions from historical data is probabilistic in nature, this leads to a machine learning problem. We then compare two tools in machine learning, namely the maximum likelihood (ML) and support vector machine (SVM), and find the former to perform better. We also provide a computationally efficient method to predict the success rate given the topology of the network, the location of the circuit breakers, and the placement of the few real-time sensors.


Observability in Power Systems

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 this work, we derive necessary and sufficient conditions for observability in a power system. Deriving sufficient conditions for observability is quite difficult and algebraic observability is often used as a surrogate tool for observability. We show that algebraic observability is necessary but not sufficient for observability. It is also shown that standard measurement sets of at least one voltage measurement, and paired active and reactive power measurements may lead to unobservability for certain measurement configurations. Using a nonlinear transformation and properties of graph theory, a set of sufficient conditions are derived for observability. These conditions are shown to be dependent on the topological properties as well as the type of available measurements. The efficiency and robustness of the proposed approach is also discussed. The proposed method can be utilized off-line as a planning tool during the initial stages of measurement system design as well as on-line prior to state estimation.

Adaptive Voltage Control of DFIG in Weak Grids

Wind turbines typically convert the wind energy into electric energy using induction generators. At every wind speed, the energy captured from the wind depends on the speed by which the blades rotate. Therefore, in order to capture the maximum available wind energy at different wind speeds, the rotation speed must be appropriately matched to the wind speed. This can be accomplished by regulating the electric power generated by the generator to be a function of the rotation speed. Earlier wind turbine technology did not provide this capability, which made the wind turbines optimal only at a certain wind speed. A more advanced technology, called doubly-fed induction generator (DFIG), does provide this capability. The wind turbine generators consist of a rotor that is mechanically connected to the blades through a gearbox, and a stator that is electrically connected to the electric grid. Power regulation in DFIG is achieved by controlling the voltage applied on the rotor circuits, whereas before these circuits were short-circuited such that no voltage was applied. For a given rotation speed, the power generated at the stator in steady state is a product of the voltage applied on the rotor and the voltage applied on the stator. Larger generators in the vicinity of the wind turbine can regulate the voltage applied on the stator such that it is always at its nominal value regardless of the power generated by the wind turbine. In this case the power generated by the wind turbine becomes a linear function of the voltage applied on the rotor. The widely used proportional-integrator (PI) controller is then able to regulate the generated power to the desired value. However, major wind resources can be found very far from the load centers, and the large generators that accompany them. The long transmission connecting the wind turbines to the main grid prevent the regulation of the voltage applied on the stator by external generators. The variability of this voltage, as a function of the power generated by the wind turbine, must then be taken into account.

We formulate a general control problem in which the output signal to be regulated is a nonlinear function of the state. We assume the parameters of this nonlinear function are unknown, but that both the state and the output signal can be measured. We then design a new controller that simultaneously estimates the parameters of the nonlinear function and drive the state such that the output signal is regulated as desired. In the manuscript listed below we prove that in the absence of noise, the output signal is indeed regulated as desired. We also describe in this manuscript how this controller can be applied to DFIG control in weak grid conditions, present its advantage over a PI-controller, and argue its robustness to measurement noise through simulation.


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 co-design a delay-aware state feedback control law. A key feature of our method is that it retain the samples of the control input until a desired time instant using shapers before releasing them for actuation. This essentially means that we regulate the delays entering our controller. Hence, unlike the traditional robust control designs reported in the power system literature, our design is delay-aware, not delay-tolerant. They are, therefore, much more reliable and practical to implement. We illustrate our results using a 50-bus, 14-generator, 4-area power system model, and show how the proposed arbitrated controller can guarantee significantly better closed-loop performance than traditional robust controllers.