Optimal Price-Energy Demand Bids for Aggregate Price-Responsive Loads

In this paper, we seek to optimally operate a retailer that, on one side, aggregates a group of price-responsive loads and on the other, submits block-wise demand bids to the day ahead and real-time markets. Such a retailer/aggregator needs to tackle uncertainty both in customer behavior and wholesale electricity markets. The goal in our design is to maximize the profit for the retailer/aggregator. We derive closed-form solutions for the risk-neutral case and also provide a stochastic optimization framework to efficiently analyze the risk-averse case. In the latter, the price-responsiveness of the load is modeled by means of a non parametric analysis of experimental random scenarios, allowing for the response model to be non-linear. The price-responsive load models are derived based on the Olympic Peninsula experiment load elasticity data. We benchmark the proposed method using data from the California ISO wholesale electricity market

Competitive and Cooperative Approaches to the Balancing Market in Distribution Grids

The electrical grid has been changing in the last decade due to the presence, at the distribution level, of renewables, distributed generation, storage systems, microgrids, and electric vehicles. The introduction of new legislation and actors in the smart grid\u2019s system opens new challenges for the activities of companies, and the development of new energy management systems, models, and methods. In order to face this revolution, new market structures are being defined as well as new technologies and optimization and control algorithms for the management of distributed resources and the coordination of local users to contribute to active power reserve and ancillary services. One of the main problems for an electricity market operator that also owns the distribution grid is to avoid congestions and maximize the quality of the service provided. The thesis concerns the development and application of new methods for the optimization of network systems (with multi-decision makers) with particular attention to the case of power distribution networks This Ph.D. thesis aims to address the current lack of properly defined market structures for the determination of balancing services in distribution networks. As a first study, to be able to handle the power flow equation in a computationally better way, a new convex relaxation has been proposed. Thereafter, two opposite types of market structure have been developed: competitive and cooperative. The first structure presents a two-tier mechanism where the market operator is in a predominant position compared to other market players. Vice versa in the cooperative mechanism (solved through distributed optimization techniques ) all actors are on the same level and work together for social welfare. The main methodological novelties of the proposed work are to solve complex problems with formally correct and computationally efficient techniques

Machine Learning Based Simulation and Control Framework for Power Distribution Systems with Transactive Elements

Transactive Energy (TE) has been recognized as a promising combination of techniques for improving the efficiency of modern power grids through market-based transactive exchanges between energy producers and energy consumers. It is of significant interest to identify optimal strategy to control the transactive load in TE systems. The behaviors of transactive loads are affected by the energy market values which in return impact the operation and stability of the distribution system. To evaluate the benefits and impacts of transactive loads and new control mechanisms, time series simulations are commonly used. These simulations consider the pricing response and the physical constraints of the system simultaneously. Such simulations are computationally demanding due to the information exchange among various participants and the complex co-simulation environments. This dissertation first explores the reduced order models to support quasi-static time-series (QSTS) simulations for power distribution systems with independent dynamic non-responsive load to address the limitations of the order reduction methods. Further, a reduced order model for transactive systems with responsive load is proposed. The proposed model consists of an aggregate responsive load (ARL) agent which utilizes two Recurrent Neural Networks (RNN) with Long Short-Term Memory units (LSTMs) to represent the transactive elements in TE systems. The developed ARL agent generates load behavior for transactive elements and interacts with the electricity market. In addition, for individual transactive elements, a control strategy for the residential Heating, Ventilation, and Air Conditioning (HVAC) is introduced through the solution of an optimization problem that balances between the energy cost and consumer’s dissatisfaction. A reinforcement learning (RL) algorithm based on Deep Deterministic Policy Gradients (DDPG) is used to obtain the optimal control strategy for the HVAC systems. The reduced order model and the DDPG RL-based control are both implemented in the Transactive Energy Simulation Platform (TESP). The reduced order model is able to produce transactive behavior very close to the full simulation model while achieving significant simulation time reduction. Moreover, simulation results demonstrated that the proposed control method for HVACs reduces the energy cost and improves the customers’ comfort simultaneously