Optimal Scheduling of Energy Storage for Energy Shifting and Ancillary Services to the Grid

This thesis is mainly focused on developing optimization-based models for scheduling of energy storage units. At first, a real-time optimal scheduling algorithm is developed seeking to maximize the storage revenue by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices. The electricity price modulation is proposed as an approach to competitively offer incentive by the utility regulator to storage to fill the gap between current and a stable rate of return. Subsequently, the application of large-scale storage for congestion relief in transmission systems as an ancillary service to the grid is investigated. An algorithm is proposed for the following objectives: (i) to generate revenue primarily by exploiting electricity price arbitrage opportunities and (ii) to optimally prepare the storage to maximize its contribution to transmission congestion relief. In addition, an algorithm is proposed to enable independently operated, locally controlled storage to accept dispatch instructions issued by Independent System Operators (ISOs). While the operation of locally controlled storage is optimally scheduled at the owner’s end, using the proposed algorithm, storage is fully dispatchable at the ISO’s end. Finally, a model is proposed and analyzed to aggregate storage benefits for a large-scale load. The complete model for optimal operation of storage-based electrical loads considering both the capital and operating expenditures of storage is developed. The applications of the proposed algorithms and models are examined using real-world market data adopted from Ontario’s electricity market and actual load information from a large-scale institutional electricity consumer in Ontario

Profitability analysis on demand-side flexibility: A review

Flexibility has emerged as an optimal solution to the increasing uncertainty in power systems produced by the continuous development and penetration of distributed generation based on renewable energy. Many studies have shown the benefits for system operators and stakeholders of diverse ancillary services derived from demand-side flexibility. Cost-benefit analysis on these flexibility services should be carried out to determine the profitable applications, as well as the required adjustments on energy market, price schemes and normative framework to maximize the positive impacts of the available flexibility. This paper endeavors to review the main topics, variables and indexes related to the profitability analysis on demand-side flexibility, as well as the influence of energy markets, pricing and standards on revenue maximization. The conclusions drawn from this review demonstrate that the profitability of flexibility services considerably de-pends on energy market structure, involved assets, electricity prices and current ancillary services remuneration.Peer ReviewedPostprint (published version

Energy Management of Prosumer Communities

The penetration of distributed generation, energy storages and smart loads has resulted in the emergence of prosumers: entities capable of adjusting their electricity production and consumption in order to meet environmental goals and to participate profitably in the available electricity markets. Significant untapped potential remains in the exploitation and coordination of small and medium-sized distributed energy resources. However, such resources usually have a primary purpose, which imposes constraints on the exploitation of the resource; for example, the primary purpose of an electric vehicle battery is for driving, so the battery could be used as temporary storage for excess photovoltaic energy only if the vehicle is available for driving when the owner expects it to be. The aggregation of several distributed energy resources is a solution for coping with the unavailability of one resource. Solutions are needed for managing the electricity production and consumption characteristics of diverse distributed energy resources in order to obtain prosumers with more generic capabilities and services for electricity production, storage, and consumption. This collection of articles studies such prosumers and the emergence of prosumer communities. Demand response-capable smart loads, battery storages and photovoltaic generation resources are forecasted and optimized to ensure energy-efficient and, in some cases, profitable operation of the resources

Enhanced power system operation with coordination and forecasting techniques

With the integration of renewable energy into power systems, traditional power systems face new challenges. Due to their inherent fluctuations and variability, the introduction of renewable energy in power systems poses new challenges in modelling uncertainty. Controlling and optimising the operation cost by adjusting the output generation of renewable energy resources makes the operation more reliable and secure. We first formulate the optimal power flow (OPF) problems for both the transmission and distribution systems and investigate the variables that greatly affect the outcome. Solving the power system optimal operation problem, we realise the importance of uncertainties involved with renewable energy due to the inherent variability of weather data. Accurate forecasting mechanisms that address their inherent intermittency and variability enable the smooth integration of such resources in power system operations. To solve this problem, in the next step, we propose a novel probabilistic framework to predict short-term PV output taking into account the variability of weather data over different days and seasons. We go beyond existing prediction methods, building a pipeline of processes, i.e., feature selection, clustering and Gaussian Process Regression (GPR). We make use of datasets that comprise power output and meteorological data such as irradiance, temperature, zenith, and azimuth. First, a correlation study is performed to select the weather features which affect solar output to a greater extent. Next, we categorise the data into four groups based on solar output and time using k-means clustering. Finally, we determine a function that relates the selected features with solar output using GPR and Matérn 5/2 as a kernel function. We validate our method with five solar generation plants in different locations and compare with the existing methodologies. More specifically, to test the proposed model, two different methods are used: (i) 5-fold cross-validation; and (ii) holding out 30 random days as test data. To confirm the model accuracy, we apply our framework 30 independent times on each of the four clusters. The average error follows a normal distribution, and with a 95% confidence level, it takes values between −1.6% to 1.4%. The proposed framework decreases the normalised root mean square error and mean absolute error by 54.6% and 55.5%, respectively, compared with other relevant works. Although we address the integration of a Microgrid into the distribution power network in the first research question, we yet need to address the transmission system constraints, as the incorporation of renewable energy into power systems poses serious challenges to the transmission and distribution power system operators (TSOs and DSOs). To fully leverage these resources, there is a need for a new market design with improved coordination between TSOs and DSOs. To answer the last research question, we propose two coordination schemes between TSOs and DSOs: one centralised and another decentralised that facilitate the integration of distributed based generation; minimise operational cost; relieve congestion; promote a sustainable system. To this end, we approximate the power equations with linearised equations so that the resulting OPFs in both the TSO and DSO become convex optimisation problems. In the resulting decentralised scheme, the TSO and DSO collaborate to allocate all resources in the system optimally. In particular, we propose an iterative bi-level optimisation technique where the upper level is the TSO that solves its own OPF and determines the locational marginal prices at substations. We demonstrate numerically that the algorithm converges to a near-optimal solution. We study the interaction of TSOs and DSOs and the existence of any conflicting objectives with the centralised scheme. More specifically, we approximate the Pareto front of the multi-objective optimal power flow problem where the entire system, i.e., transmission and distribution systems, is modelled. The proposed ideas are illustrated through a five-bus transmission system connected with distribution systems, represented by the IEEE 33- and 69-bus feeders

Optimal management of a virtual power plant with photovoltaic and power-to-gas to exploit the benefit of value stacking from crossmarket arbitrage

The energy sector is facing a transformation, the traditional business model for electricity generated by large, centralised plants with limited customer engagement and standardized supply contracts is fading away. The restyling of the electricity markets is a consequence of several factors: the liberalisation of the electricity sector begun around 20 years ago in Italy; the growth of the intermittent and unpredictable renewable technologies thanks to lower costs and larger investments than fossil fuels ones; the spread of distributed generation that makes the consumer able to produce energy too, which makes him an active player in the market by becoming a so-called prosumer. In this context, given the dynamism to which the electricity market is subjected, it is interesting to study the economic feasibility of enhanced bidding strategies from the point of view of the manager of a plant consisting of photovoltaic and Power-to-Gas. The starting point of this thesis is a code formulated by the research group from the Department of Industrial Engineering at the University of Padua which comprehends Jan Marc Schwidtal, Marco Agostini, Massimiliano Coppo, Fabio Bignucolo and Arturo Lorenzoni. Specifically, the research work models the operation of a virtually aggregated plant by highlighting the opportunities arising from the value stacking in terms of progressive market penetration of this unit. It evaluates energy flows and financial results on annual basis, taking into account technical constraints of the photovoltaic generation and of the Power-to-gas specifications. In this thesis, changes have been introduced concerning only the description of the economic side of the model and not the technical one. The idea is to implement an enhanced optimization approach to formulate a combined bidding strategy across the energy markets and the auxiliary services markets, exploiting the concept of cross-market arbitrage: this method regards in particular the intraday and balancing markets and consists in buying and subsequently reselling the same type of energy in the same quantity at two different prices. Four different operating modes with a gradual and increasing integration in the markets are studied and the respective optimization problems are solved using the Gurobi solver through the Yalmip toolbox installed within the Matlab software. Lastly, considerations were drawn about the risk management that could affect the manager of the unit by investigating how far it is possible to go in adopting this bidding strategy while operating the plant.The energy sector is facing a transformation, the traditional business model for electricity generated by large, centralised plants with limited customer engagement and standardized supply contracts is fading away. The restyling of the electricity markets is a consequence of several factors: the liberalisation of the electricity sector begun around 20 years ago in Italy; the growth of the intermittent and unpredictable renewable technologies thanks to lower costs and larger investments than fossil fuels ones; the spread of distributed generation that makes the consumer able to produce energy too, which makes him an active player in the market by becoming a so-called prosumer. In this context, given the dynamism to which the electricity market is subjected, it is interesting to study the economic feasibility of enhanced bidding strategies from the point of view of the manager of a plant consisting of photovoltaic and Power-to-Gas. The starting point of this thesis is a code formulated by the research group from the Department of Industrial Engineering at the University of Padua which comprehends Jan Marc Schwidtal, Marco Agostini, Massimiliano Coppo, Fabio Bignucolo and Arturo Lorenzoni. Specifically, the research work models the operation of a virtually aggregated plant by highlighting the opportunities arising from the value stacking in terms of progressive market penetration of this unit. It evaluates energy flows and financial results on annual basis, taking into account technical constraints of the photovoltaic generation and of the Power-to-gas specifications. In this thesis, changes have been introduced concerning only the description of the economic side of the model and not the technical one. The idea is to implement an enhanced optimization approach to formulate a combined bidding strategy across the energy markets and the auxiliary services markets, exploiting the concept of cross-market arbitrage: this method regards in particular the intraday and balancing markets and consists in buying and subsequently reselling the same type of energy in the same quantity at two different prices. Four different operating modes with a gradual and increasing integration in the markets are studied and the respective optimization problems are solved using the Gurobi solver through the Yalmip toolbox installed within the Matlab software. Lastly, considerations were drawn about the risk management that could affect the manager of the unit by investigating how far it is possible to go in adopting this bidding strategy while operating the plant

Machine Learning based Wind Power Forecasting for Operational Decision Support

To utilize renewable energy efficiently to meet the needs of mankind's living demands becomes an extremely hot topic since global warming is the most serious global environmental problem that human beings are facing today. Burning of fossil fuels, such as coal and oil directly for generating electricity leads to environment pollution and exacerbates global warning. However, large-scale development of hydropower increases greenhouse gas emissions and greenhouse effects. This research is related to knowledge of wind power forecasting (WPF) and machine learning (ML). This research is built around one central research question: How to improve the accuracy of WPF by using AI methods? A pilot conceptual system combining meteorological information and operations management has been formulated. The main contribution is visualized in a proposed new framework, named Meteorological Information Service Decision Support System, consisting of a meteorological information module, wind power prediction module and operations management module. This conceptual framework has been verified by quantitative analysis in empirical cases. This system utilizes meteorological information for decision-making based on condition-based maintenance in operations and management for the purpose of optimizing energy management. It aims to analyze and predict the variation of wind power for the next day or the following week to develop scheduling planning services for WPEs based on predicting wind speed for every six hours, which is short-term wind speed prediction, through training, validating, and testing dataset. Accurate prediction of wind speed is crucial for weather forecasting service and WPF. This study presents a carefully designed wind speed prediction model which combines fully-connected neural network (FCNN), long short-term memory (LSTM) algorithm with eXtreme Gradient Boosting (XGBoost) technique, to predict wind speed. The performance of each model is tested by using reanalysis data from European Center for Medium-Range Weather Forecasts (ECMWF) for Meteorological observatory located in Vaasa in Finland. The results show that XGBoost algorithm has similar improved prediction performance as LSTM algorithm, in terms of RMSE, MAE and R2 compared to the commonly used traditional FCNN model. On the other hand, the XGBoost algorithm has a significant advantage on training time while comparing to the other algorithms in this case study. Additionally, this sensitivity analysis indicates great potential of the optimized deep learning (DL) method, which is a subset of machine learning (ML), in improving local weather forecast on the coding platform of Python. The results indicate that, by using Meteorological Information Service Decision Support System, it is possible to support effective decision-making and create timely actions within the WPEs. Findings from this research contribute to WPF in WPEs. The main contribution of this research is achieving decision optimization on a decision support system by using ML. It was concluded that the proposed system is very promising for potential applications in wind (power) energy management