A distributed model predictive control strategy for back-to-back converters

In recent years Model Predictive Control (MPC) has been successfully used for the control of power electronics converters with different topologies and for different applications. MPC offers many advantages over more traditional control techniques such as the ability to avoid cascaded control loops, easy inclusion of constraint and fast transient response. On the other hand, the controller computational burden increases exponentially with the system complexity and may result in an unfeasible realization on modern digital control boards. This paper proposes a novel Distributed Model Predictive Control, which is able to achieve the same performance of the classical Model Predictive Control whilst reducing the computational requirements of its implementation. The proposed control approach is tested on a AC/AC converter in a back-to-back configuration used for power flow management. Simulation results are provided and validated through experimental testing in several operating conditions

Optimizing daily operation of battery energy storage systems under real-time pricing schemes

Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations

Cooperation-driven distributed model predictive control for energy storage systems

In this letter, a distributed model predictive control strategy for battery energy storage systems is proposed to regulate voltage in distribution network with high-renewable penetration. Control actions are calculated based on communication between interconnected neighboring subsystems and a multistep receding optimization, also considering system and battery constraints. The proposed approach is shown to be highly effective through a simulation case study, indicating high potential for applications

ENERGY & STORAGE SHARING STRATEGIES IN AN ELECTRICITY MARKET ENVIRONMENT

The rapid growth of renewable energy generation (REG) and energy storage systems (ESS) has created a need to further develop the electricity market for distributed energy, to stimulate the technology and application of REG and battery energy storage systems (BESS). Considering that the investment cost is still high at this stage, a window of opportunity exists for the development of a sharing economy. In light of this, this thesis focuses on energy and storage sharing strategies in an electricity market environment. A distributed energy sharing strategy is proposed for a peer-to-peer (P2P) model on a microgrid. In addition, the pricing model for users in this proposed strategy has been optimised using game theory—with the Bayesian Nash Equilibrium (GM-BNE) algorithm. Based on the basic call auction trading model, the energy trading mechanism has been modified. Meanwhile, an energy sharing cloud service is proposed based on a decentralised approach, in which the cloud energy management strategy can be customised for each participant. Rigorous proofs are also given. A detailed energy storage sharing strategy of the hybrid electricity and gas energy is proposed in the distribution network, which considers the energy operation of BESS and thermal energy storage system (TESS). The techno-economic analysis based on the BESS and TESS sizing model is conducted for storage sharing between users. When considering the battery firm in the joint storage sharing strategy, a novel sharing model is proposed based on the classic per-use sharing economy business model. Rigorous mathematical proofs are given for the application of the sharing economy model to BESS, in which the sharing pricing model is validated for technical feasibility and accuracy. The proposed energy and storage sharing strategies are applicable to distributed users, in the cases of the hospitality industry and smart home. The proposed sharing strategies are also beneficial for investors, as demonstrated in the case for a battery firm. In the case of the battery firm, this per-use rental service can open new benefits. The case studies results show that the proposed energy and storage sharing strategies provide a 'win-win' situation for customers, the battery sales firm and energy networks

Bayesian Learning Strategies in Wireless Networks

This thesis collects the research works I performed as a Ph.D. candidate, where the common thread running through all the works is Bayesian reasoning with applications in wireless networks. The pivotal role in Bayesian reasoning is inference: reasoning about what we don’t know, given what we know. When we make inference about the nature of the world, then we learn new features about the environment within which the agent gains experience, as this is what allows us to benefit from the gathered information, thus adapting to new conditions. As we leverage the gathered information, our belief about the environment should change to reflect our improved knowledge. This thesis focuses on the probabilistic aspects of information processing with applications to the following topics: Machine learning based network analysis using millimeter-wave narrow-band energy traces; Bayesian forecasting and anomaly detection in vehicular monitoring networks; Online power management strategies for energy harvesting mobile networks; Beam training and data transmission optimization in millimeter-wave vehicular networks. In these research works, we deal with pattern recognition aspects in real-world data via supervised/unsupervised learning methods (classification, forecasting and anomaly detection, multi-step ahead prediction via kernel methods). Finally, the mathematical framework of Markov Decision Processes (MDPs), which also serves as the basis for reinforcement learning, is introduced, where Partially Observable MDPs use the notion of belief to make decisions about the state of the world in millimeter-wave vehicular networks. The goal of this thesis is to investigate the considerable potential of inference from insightful perspectives, detailing the mathematical framework and how Bayesian reasoning conveniently adapts to various research domains in wireless networks