Novel probabilistic optimization model for lead-acid and vanadium redox flow batteries under real-time pricing programs

The integration of storage systems into smart grids is being widely analysed in order to increase the flexibility of the power system and its ability to accommodate a higher share of wind and solar power. The success of this process requires a comprehensive techno-economic study of the storage technology in contrast with electricity market behaviour. The focus of this work is on lead-acid and vanadium redox flow batteries. This paper presents a novel probabilistic optimization model for managing energy storage systems. The model is able to incorporate the forecasting error of electricity prices, offering with this a near-optimal control option. Using real data from the Spanish electricity market from the year 2016, the probability distribution of forecasting error is determined. The model determines electricity price uncertainty by means of Monte Carlo Simulation and includes it in the energy arbitrage problem, which is eventually solved by using an integer-coded genetic algorithm. In this way, the probability distribution of the revenue is determined with consideration of the complex behaviours of lead-acid and vanadium redox flow batteries as well as their associated operating devices such as power converters

Demand-Side Management for Energy-efficient Data Center Operations with Renewable Energy and Demand Response

In recent years, we have noticed tremendous increase of energy consumption and carbon pollution in the industrial sector, and many energy-intensive industries are striving to reduce energy cost and to have a positive impact on the environment. In this context, this dissertation is motivated by opportunities to reduce energy cost from demand-side perspective. Specifically, industries have an opportunity to reduce energy consumption by improving energy-efficiency in their system operations. By improving utilization of their resources, they can reduce waste of energy, and thus, they are able to prevent paying unnecessary energy cost. In addition, because of today‘s high penetration of renewable generation (e.g. wind or solar), many industries consider renewable energy as a promising solution to reduce energy cost and carbon pollution, and they have tried to utilize renewable energy to meet their power demand by installing on-site generation facilities (e.g. PV panels on roof top) or making a contract with renewable generation farms. Moreover, it is becoming common for energy markets to allow industries to directly purchase electricity from them while providing the industries with day-ahead and real-time electricity price information. In this situation, industries have an opportunity to adjust purchase and consumption of energy in response to time-varying electricity price and intermittent renewable generation to reduce their energy procurement cost, which are called demand response. Considering these opportunities, it is anticipated that the industrial sector can save a significant amount of energy cost, however, time-varying behavior, uncertainty and stochasticity in system operations, power demand, renewable energy, and electricity price make it challenging to determine optimal operational decision. Motivated by the aforementioned opportunities as well as challenges, this dissertation focuses on developing decision-making methodologies tailored for demand-side energy system operations to improve energy-efficiency based on energy-aware system operations and reduce energy procurement cost by utilizing renewable energy and demand response in an integrated fashion to optimally reduce energy cost. For practical application, this dissertation considers real-world practices in data centers including their operations management and power procurement for the following research tasks: (i) develop a server provisioning algorithm that dynamically adapts server operations in response to heterogeneous and time-varying workloads to reduce energy consumption while providing performance guarantees based on time-stability; (ii) propose stochastic optimization models for optimal energy procurement to determine purchase and consumption of energy based on day-ahead and real-time energy market operations considering utilization of renewable energy based on demand response; (iii) suggest a decision-making model that integrate the proposed server provisioning algorithm with energy procurement to achieve energy-efficiency in data center operations to reduce both energy consumption and energy cost against variability and uncertainty. In terms of methodologies, this study uses operations research techniques including deterministic and stochastic models, such as, queueing analysis, mixed-integer program, Markov decision process, two-stage stochastic program, and probabilistic constrained program. In conclusion, this dissertation claims that renewable energy, demand response, and energy storage are worth to be considered for data center operations to reduce energy consumption and procurement cost. Although variability and uncertainty in system operations, renewable generation, and electricity price make it challenging to determine optimal operational decisions, numerical results show that the proposed optimization problems can be efficiently solved by the developed algorithm. The proposed decision-making methodologies can also be extended to other industries, and thus, this dissertation study would be a good starting point to study demand-side management in energy system operations

Cooperation of storage operation in a power network with renewable generation

In this paper, we seek to properly schedule the operation of multiple storage devices so as to minimize the expected total cost (of conventional generation) in a power network with intermittent renewable generation. Since the power network constraints make it intractable to compute optimal storage operation policies through dynamic programming-based approaches, we propose a Lyapunov optimization-based online algorithm (LOPN), which makes decisions based only on the current state of the system (i.e., the current demand and renewable generation). The proposed algorithm is computationally simple and achieves asymptotic optimality (as the capacity of energy storage grows large). To improve the performance of the LOPN algorithm for the case with limited storage capacity, we propose a threshold-based energy storage management (TESM) algorithm that utilizes the forecast information (on demand and renewable generation) over the next a few time slots to make storage operation decisions. Numerical experiments are conducted on IEEE 6- and 9-bus test systems to validate the asymptotic optimality of LOPN, and compare the performance of LOPN and TESM. Numerical results show that TESM significantly outperforms LOPN when the storage capacity is relatively small

A Novel Reinforcement Learning-Optimization Approach for Integrating Wind Energy to Power System with Vehicle-to-Grid Technology

High integration of intermittent renewable energy sources (RES), specifically wind power, has created complexities in power system operations due to their limited controllability and predictability. In addition, large fleets of Electric Vehicles (EVs) are expected to have a large impact on electricity consumption, contributing to the volatility. In this dissertation, a well-coordinated smart charging approach is developed that utilizes the flexibility of EV owners in a way where EVs are used as distributed energy storage units and flexible loads to absorb the fluctuations in the wind power output in a vehicle-to-grid (V2G) setup. Challenges for people participation in V2G, such as battery degradation and insecurity about unexpected trips, are also addressed by using an interactive mechanism in smart grid. First, a static deterministic model is formulated using multi-objective mixed-integer quadratic programming (MIQP) assuming known parameters day ahead of time. Subsequently, a formulation for real-time dynamic schedule is provided using a rolling-horizon with expected value approximation. Simulation experiments demonstrate a significant increase in wind utilization and reduction in charging cost and battery degradation compared to an uncontrolled charging scenario. Formulating the scheduling problem of the EV-wind integrated power system using conventional stochastic programming (SP) approaches is challenging due to the presence of many uncertain parameters with unknown underlying distributions, such as wind, price, and different commuting patterns of EV owners. To alleviate the problem, a model-free Reinforcement Learning (RL) algorithm integrated with deterministic optimization is proposed that can be applied on many multi-stage stochastic problems while mitigating some of the challenges of conventional SP methods (e.g., large scenario tree, computational complexity) as well as the challenges in model-free RL (e.g., slow convergence, unstable learning in dynamic environment). The simulation results of applying the combined approach on the EV scheduling problem demonstrate the effectiveness of the RL-Optimization method in solving the multi-stage EV charge/discharge scheduling problem. The proposed methods perform better than standard RL approaches (e.g., DDQN) in terms of convergence speed and finding the global optima. Moreover, to address the curse of dimensionality issue in RL with large action-state space, a heuristic EV fleet charging/discharging scheme is used combined with RL-optimization approach to solve the EV scheduling problem for a large number of EVs

Energy Supplies in the Countries from the Visegrad Group

The purpose of this Special Issue was to collect and present research results and experiences on energy supply in the Visegrad Group countries. This research considers both macroeconomic and microeconomic aspects. It was important to determine how the V4 countries deal with energy management, how they have undergone or are undergoing energy transformation and in what direction they are heading. The articles concerned aspects of the energy balance in the V4 countries compared to the EU, including the production of renewable energy, as well as changes in its individual sectors (transport and food production). The energy efficiency of low-emission vehicles in public transport and goods deliveries are also discussed, as well as the energy efficiency of farms and energy storage facilities and the impact of the energy sector on the quality of the environment