10h30 - 10h55
Optimally Scheduling Public Safety Power Shutoffs
In an effort to reduce power system-caused wildfires, utilities carry out public safety power shutoffs (PSPS) in which portions of the grid are de-energized to mitigate the risk of ignition. The decision to call a PSPS must balance reducing ignition risks and the negative impact of service interruptions. In this work, we consider three PSPS scheduling scenarios, which we model as dynamic programs. In the first two scenarios, we assume that N PSPSs are budgeted as part of the investment strategy. In the first scenario, a penalty is incurred for each PSPS declared past the Nth event. In the second, we assume that some costs can be recovered if the number of PSPSs is below N while still being subject to a penalty if above N. In the third, the system operator wants to minimize the number of PSPS such that the total expected cost is below a threshold. We provide optimal or asymptotically optimal policies for each case, the first two of which have closed-form expressions. Lastly, we show the equivalence between the first PSPS model and critical-peak pricing, and obtain an optimal scheduling policy to reduce the peak demand based on weather observations.
10h55 - 11h20
Trilayer ADMM for decentralized EV charging scheduling
The integration of Electric Vehicles (EVs) into the power grid is a challenging task which requires a proper EV charging control method. One of the key challenges in this concept is defining a comprehensive control structure that is scalable to accommodate large EV fleets. In this presentation, a distributed trilayer multi-agent framework based on the Alternating Direction Method of Multipliers (ADMM) is presented for optimal EV charging scheduling. Trilayer ADMM is a decentralized hierarchical optimization algorithm that transforms the centralized optimal fleet charging problem to individual optimization problems and distributes them across EVs, EV aggregators (EVAs) and distribution network operator (DNO) resulting in computational scalability. Since the individual problems are coupled, they are solved iteratively by transmitting incentive signals between the agents. The framework can be parameterized to balance the importance of DNO, fleet and individual EV goals. We show how EV ADMM can be applied to control an EV fleet to meet objectives such as demand valley filling, lower charging costs and reduced battery depreciation costs. Due to its flexibility and scalability, EV ADMM offers a practical solution for optimal EV fleet control. The significant outcome is the accommodation of a large-scale EV fleet without investing in grid expansion.
11h20 - 11h45
A Novel Mean Field Game-Based Strategy for Charging Electric Vehicles in Solar Powered Parking Lots
We develop a strategy, with concepts from Mean Field Games (MFG), to coordinate the charging of a large population of battery electric vehicles (BEVs) in a parking lot powered by solar energy and managed by an aggregator. A yearly parking fee is charged for each BEV irrespective of the amount of energy extracted. The goal is to share the energy available so as to minimize the standard deviation (STD) of the state of charge (SOC) of batteries when the BEVs are leaving the parking lot, while maintaining some fairness and decentralization criteria. The MFG charging laws correspond to the Nash equilibrium induced by quadratic cost functions based on an inverse Nash equilibrium concept and designed to favor the batteries with the lower SOCs upon arrival. While the MFG charging laws are strictly decentralized, they guarantee that a mean of instantaneous charging powers to the BEVs follows a trajectory based on the solar energy forecast for the day. That day ahead forecast is broadcasted to the BEVs which then gauge the necessary SOC upon leaving their home. We illustrate the advantages of the MFG strategy for the case of a typical sunny day and a typical cloudy day when compared to more straightforward strategies: first come first full/serve and equal sharing. The behavior of the charging strategies is contrasted under conditions of random arrivals and random departures of the BEVs in the parking lot.
11h45 - 12h10
Cooperative Hierarchical Coordination of Networked aggregators with Bidding Scheme
We introduce a new three level hierarchical schema with a demand reduction biding strategy to control and optimize the distribution of energy in a smart grid in a 24-hour period. We propose an efficient bidding strategy that involves different local aggregators that act as brokers between the Distribution System Operator and different energy consumers. We propose a central residential aggregator that controls a set of local residential aggregators who manage the schedule of associated clients’ devises and generate a list of bids according to different power limits. Similarly, we introduce an electric vehicle aggregator that manages the smart charging of the electric vehicles and submits a set of bids under a list of power reduction. The Distribution System Operators is the final decision maker who choose the best bids combination to optimize the market power demand. We also adopt a photovoltaic panel as a power-producing system and a battery storage system that aims to reduce and smooth the aggregated power demand. Incentives are provided to different aggregators and users for their participation while ensuring end-user satisfaction and comfort. The results show that the DSO reduces 27% of the total peak power demand.