Biblio

An auction-based game is formulated for coordinating the charging of a population of electric vehicles (EVs) over a finite horizon. The proposed auction requires individual EVs to submit bid profiles that have dimension equal to two times the number of time-steps in the horizon. They compete for energy allocation at each time-step. Use of the progressive second price (PSP) auction mechanism ensures that incentive compatibility holds for the auction game. However, due to cross-elasticity between the charging time-steps, the marginal valuation of an individual EV at a particular time is determined by both the demand at that time and the total demand over the entire horizon. This difficulty is addressed by partitioning the allowable set of bid profiles according to the total desired energy over the entire horizon. It is shown that the efficient bid profile over the charging horizon is a Nash equilibrium of the underlying auction game. A dynamic update mechanism for the auction game is designed. A numerical example demonstrates that the auction system converges to the efficient Nash equilibrium.

It has been a hot research topic to research the incorporation of large-scale PEVs into smart grid, such as the valley-fill strategy. However high charging rates under the valley-fill behavior may result in high battery degradation cost. Consequently in this paper, we novelly setup a framework to study a class of charging coordination problems which deals with the tradeoff between total generation cost and the accumulated battery degradation costs for all PEVs during a multi-time interval. Due to the autonomy of individual PEVs and the computational complexity for the system with large-scale PEV populations, it is impractical to implement the solution in a centralized way. Alternatively we propose a novel decentralized method such that each individual submits a charging profile, with respect to a given fixed price curve, which minimizes its own cost dealing with the tradeoff between the electricity cost and battery degradation cost over the charging interval; the price curve is updated based upon the aggregated PEV charging profiles. We show that, following the proposed decentralized price update procedure, the system converges to the unique efficient (in the sense of social optimality) solution under certain mild conditions.

Minimizing the grid impacts of large-scale plug-in electric vehicle (PEV) charging tends to be associated with coordination strategies that seek to fill the overnight valley in electricity demand. However such strategies can result in high charging power, raising the possibility of local overloads within the distribution grid and of accelerated battery degradation. The paper establishes a framework for PEV charging coordination that facilitates the tradeoff between total generation cost and the local costs associated with overloading and battery degradation. A decentralized approach to solving the resulting large-scale optimization problem involves each PEV minimizing their charging cost with respect to a forecast price profile while taking into account local grid and battery effects. The charging strategies proposed by participating PEVs are used to update the price profile which is subsequently rebroadcast to the PEVs. The process then repeats. It is shown that under mild conditions this iterative process converges to the unique, efficient (socially optimal) coordination strategy.

The expanding penetration of non-dispatchable renewable resources within power system generation portfolios is motivating the development of demand-side strategies for balancing generation and load. Commercial heating, ventilation, and air conditioning (HVAC) loads are potential candidates for providing such demand-response (DR) services as they consume significant energy and because of the temporal flexibility offered by their inherent thermal inertia. Several ancillary services markets have recently opened up to participation by DR resources, provided they can satisfy certain performance metrics. We discuss different control strategies for providing frequency regulation DR from commercial HVAC systems and components, and compare performance results from experiments and simulation. We also present experimental results from a single  30,000-m2 office building and quantify the DR control performance using standardized performance criteria. Additionally, we evaluate the cost of delivering this service by comparing the energy consumed while providing DR against a counterfactual baseline.

Electrical power distribution networks are susceptible to Fault Induced Delayed Voltage Recovery (FIDVR). Such events are usually initiated by faults at substations and lead to sustained low voltages throughout the distribution grid. The mechanism underpinning this phenomenon is known to be the stalling of induction motors in residential air conditioners. It is useful to be able to partition parameter space into parameters that induce motor stalling versus those for which the motors recover for a particular fault. Novel algorithms are presented for numerically computing the border that separates such parameter-space partitions, and for finding the point on the border that is closest to a given point in parameter space. These algorithms are justified by theoretical results which exploit the presence of a special equilibrium point on the state-space stability boundary, called the controlling unstable equilibrium point. The key idea is to vary parameters in order to drive the trajectory to spend a fixed amount of time inside a ball centered at the controlling unstable equilibrium point, and then to maximize the amount of time inside that ball. The algorithms are applied to a modified version of the IEEE 37-bus test feeder.

While renewable generation sources provide many economic and environmental benefits for the operation of power systems, their inherent stochastic nature introduces challenges from the perspective of reliability. Existing optimal power flow (OPF) methods must therefore be extended to consider forecast errors to mitigate in an economic manner the uncertainty that renewable generation introduces. This paper presents an AC-QP OPF solution algorithm that has been modified to include wind generation uncertainty. We solve the resulting stochastic optimization problem using a scenario based algorithm that is based on randomized methods that provide probabilistic guarantees of the solution. The proposed method produces an AC-feasible solution while satisfying reasonable reliability criteria. Test cases are included for the IEEE 14-bus network that has been augmented with 2 wind generators. The scalability, optimality and reliability achieved by the proposed method are then assessed.

A novel class of auction-based games is formulated to study coordination problems arising from charging a population of electric vehicles (EVs) over a finite horizon. To compete for energy allocation over the horizon, each individual EV submits a multidimensional bid, with the dimension equal to two times the number of time-steps in the horizon. Use of the progressive second price (PSP) auction mechanism ensures that incentive compatibility holds for the auction games. However, due to the cross elasticity of EVs over the charging horizon, the marginal valuation of an individual EV at a particular time is determined by both the demand at that time and the total demand over the entire horizon. This difficulty is addressed by partitioning the allowable set of bid profiles based on the total desired energy over the entire horizon. It is shown that the efficient bid profile over the charging horizon is a Nash equilibrium of the underlying auction game. An update mechanism for the auction game is designed. A numerical example demonstrates that the auction process converges to an efficient Nash equilibrium. The auction-based charging coordination scheme is adapted to a receding horizon formulation to account for disturbances and forecast uncertainty.

Renewable generation and energy storage are playing an ever increasing role in power systems. Hence, there is a growing need for integrating these resources into the optimal power flow (OPF) problem. While storage devices are important for mitigating renewable variability, they introduce temporal coupling in the OPF constraints, resulting in a multiperiod OPF formulation. This paper explores a solution method for multiperiod AC OPF that combines a successive quadratic programming approach (AC-QP) with a second-order cone programming (SOCP) relaxation of the OPF problem. The SOCP relaxation’s solution is used to initialize the AC-QP OPF algorithm. Additionally, the lower bound on the objective value obtained from the SOCP relaxation provides a measure of solution quality. This combined method is demonstrated on several test cases with up to 4259 nodes and a time horizon of 8 time steps. A comparison of initialization schemes indicates that the SOCP-based approach offers improved convergence rate, execution time and solution quality.

Enhanced control capabilities are required to coordinate the response of increasingly diverse controllable resources, including FACTS devices, energy storage, demand response and fast-acting generation. Model-predictive control (MPC) has shown great promise for accommodating these devices in a corrective control framework that exploits the thermal overload capability of transmission lines and limits detrimental effects of contingencies. This work expands upon earlier implementations by incorporating voltage magnitudes and reactive power into the system model utilized by MPC. These improvements provide a more accurate prediction of system behavior and enable more effective control decisions. Performance of this enhanced MPC strategy is demonstrated using a model of the Californian power system containing 4259 buses. Sparsity in modelling and control actions must be exploited for implementation on large networks. A method is developed for identifying the set of controls that is most effective for a given contingency. The proposed MPC corrective control algorithm fits naturally within energy management systems where it can provide feedback control or act as a guide for system operators by identifying beneficial control actions across a wide range of devices.

It is common for power system behavior to be expressed in terms of polar voltage coordinates. When applied in optimization settings, loss formulations in polar voltage coordinates typically assume that voltage magnitudes are fixed. In reality, voltage magnitudes vary and may have an appreciable effect on losses. This paper proposes a systematic approach to incorporating the effects of voltage magnitude changes into a linear relaxation of the losses on a transmission line. This approach affords greater accuracy when describing losses around a base voltage condition as compared to previous linear and piecewise linear methods. It also better captures the true behavior of losses at conditions away from the flat voltage profile.

As the population of electric vehicles (EVs) grows, coordinating their charging over a finite time horizon will become increasingly important. Recent work established a framework for EV charging coordination where a central node broadcast a price signal that facilitated the tradeoff between the total generation cost and local costs associated with battery degradation and distribution network overloading. This paper considers a completely distributed protocol where the central node is eliminated. Instead, a consensus algorithm is used to fully distribute the price update mechanism. Each EV computes a local price through its estimate of the total EV charging demand, and exchanges this information with its neighbours. A consensus algorithm establishes the average over all the EV-based prices. It is shown that under a reasonable assumption, the price update mechanism is a Krasnoselskij iteration, and this iteration is guaranteed to converge to a fixed point. Furthermore, this iterative process converges to the unique and efficient solution.

This paper considers the coordination of controlled loads in a framework that loads connect to the distribution network through transformers. Our objective is designing a decentralized control method that can motivate selfish loads to achieve global benefits. We formulate this problem as a hierarchical model. In the lower level, each transformer broadcasts a price signal to the loads connect to it, under which loads implement individual best strategies. While in the upper level, transformers communicate with the distribution network and obtain a price reflecting the system generation cost. Each transformer determines a price including this price and another part reflecting individual characteristics. By proposing a dynamic update algorithm, our results build that the system converges to the unique and efficient solution with fast convergence speed.

Transactive or market-based coordination strategies have recently been proposed to control the aggregate demand of a large number of electric loads. While several operational benefits can be achieved, such as reducing the demand below distribution feeder capacity limits and providing users with flexibility to consume energy based on the price they are willing to pay, our work focuses on studying the impact of market based coordination mechanisms on load synchronization and power oscillations. We adopt the transactive energy framework and apply it to a population of thermostatically controlled loads (TCLs). We present a modified TCL switching logic that takes into account market coordination signals, alongside the natural switching conditions. Our studies suggest that several factors, in a market-based coordination mechanism, could contribute to load synchronism, including sharp changes in market prices broadcast to loads, lack of diversity in user specified bid curves, feeder limits being encountered periodically and being set too low, and the form of user bid curves. All these factors can contribute in various ways to synchronization of TCL behavior and lead to power oscillations. The case studies provide novel insights into challenges associated with market-based coordination strategies, thereby providing a basis for modifications that address those issues.

Aggregate models are used in the analysis and control of large populations of thermostatically controlled loads (TCLs), such as air-conditioners and water heaters. The fidelity of such models is studied by analyzing the influences of noise and parameter heterogeneity on TCL aggregate dynamics. While TCLs can provide valuable services to the power systems, control may cause their temperatures to synchronize, which may then lead to undesirable power oscillations. Recent works have shown that the aggregate dynamics of TCLs can be modeled by tracking the evolution of probability densities over discrete temperature ranges or bins. To accurately capture oscillations in aggregate power, such bin-based models require a large number of bins. The process of obtaining the Markov state transition matrix that governs the dynamics can be computationally intensive when using Monte Carlo based system identification techniques. Existing analytical techniques are further limited as noise and heterogeneity in several thermal parameters are difficult to incorporate. These challenges are addressed by developing a fast analytical technique that incorporates noise and heterogeneity into bin-based aggregate models. Results show the identified and the analytical models match very closely. Studies consider the influence of model error, noise and parameter heterogeneity on the damping of oscillations. Results demonstrate that for a specific bin width, the model can be invariant to quantifiable levels of noise and parameter heterogeneity. Finally, a discussion is provided of cases where existing bin models may face challenges in capturing the influence of heterogeneity.