Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development

Artificial Intelligence for Resilience in Smart Grid Operations

Today, the electric power grid is transforming into a highly interconnected network of advanced technologies, equipment, and controls to enable a smarter grid. The growing complexity of smart grid requires resilient operation and control. Power system resilience is defined as the ability to harden the system against and quickly recover from high-impact, low-frequency events. The introduction of two-way flows of information and electricity in the smart grid raises concerns of cyber-physical attacks. Proliferated penetration of renewable energy sources such as solar photovoltaic (PV) and wind power introduce challenges due to the high variability and uncertainty in generation. Unintentional disruptions and power system component outages have become a threat to real-time power system operations. Recent extreme weather events and natural disasters such as hurricanes, storms, and wildfires demonstrate the importance of resilience in the power system. It is essential to find solutions to overcome these challenges in maintaining resilience in smart grid. In this dissertation, artificial intelligence (AI) based approaches have been developed to enhance resilience in smart grid. Methods for optimal automatic generation control (AGC) have been developed for multi-area multi-machine power systems. Reliable AI models have been developed for predicting solar irradiance, PV power generation, and power system frequencies. The proposed short-horizon AI prediction models ranging from few seconds to a minute plus, outperform the state-of-art persistence models. The AI prediction models have been applied to provide situational intelligence for power system operations. An enhanced tie-line bias control in a multi-area power system for variable and uncertain environments has been developed with predicted PV power and bus frequencies. A distributed and parallel security-constrained optimal power flow (SCOPF) algorithm has been developed to overcome the challenges in solving SCOPF problem for large power networks. The methods have been developed and tested on an experimental laboratory platform consisting of real-time digital simulators, hardware/software phasor measurement units, and a real-time weather station

The reservoir network: A new network topology for district heating and cooling

Thermal district networks are effective solutions to substitute fossil fuels with renewable energy sources for heating and cooling. Moreover, thermal networking of buildings allows energy efficiency to be further increased. The waste heat from cooling can be reused for heating in thermal district systems. Because of bidirectional energy flows between prosumers, thermal networks require new hydraulic concepts. In this work, we present a novel network topology for simultaneous heating and cooling: the reservoir network. The reservoir network is robust in operation due to hydraulic decoupling of transfer stations, integrates heat sources and heat sinks at various temperature levels and is flexible in terms of network expansion. We used Modelica simulations to compare the new single-pipe reservoir network to a basecase double-pipe network, taking yearly demand profiles of different building types for heating and cooling. The electric energy consumed by the heat pumps and circulations pumps differs between the reservoir and base case networks by less than 1%. However, if the reservoir network is operated with constant instead of variable mass flow rate, the total electrical consumption can increase by 48% compared to the base case. As with any other network topology, the design and control of such networks is crucial to achieving energy efficient operation. Investment costs for piping and trenching depend on the district layout and dimensioning of the network. If a ring layout is applied in a district, the reservoir network with its single-pipe configuration is more economical than other topologies. For a linear layout, the piping costs are slightly higher for the reservoir network than for the base case because of larger pipe diameters

Smart Senja electrical network expansion modeling

The addition of variable renewable energy sources into the electrical energy systems of the world has been increasing in recent years. This form of distributed energy production with high production volatility can introduce massive challenges in operating a lower voltage distribution network. One of these affected networks is on the island of Senja in northern Norway, with an eldering radial electrical network with a single connection to the national transmission grid. In this study, prescriptive analysis of the network through mathematical optimization is implemented to investigate if there are more effective solutions to this problem other than building more electrical lines. In selected parts of the island, the electrical network experiences electrical faults of different magnitude and concern affecting 1500 hours a year. In this thesis, the model GenX is presented which prescribes solutions reducing these faults to zero while also cutting costs compared to the baseline scenario of today’s system. Results from the model indicate that simple installments of distributed power generation in conjunction with electrical energy storage drastically improve network capacity and industrial expansion opportunities. Also investigated is the feasibility of operating the electrical network on the island without any connection to the external grid. Meant as a proof of concept for the application of mathematical optimization on electrical grids in other more remote parts of the world. The model proves that investments in local electricity production positively impact the system at a fraction of the cost of building new regional distribution infrastructure. Finally, some drawbacks of the chosen analytical tool used to construct the mathematical optimization model are presented alongside selected methods applicable to apprehend or circumvent these limitations

A novel mechanical analogy based battery model for SoC estimation using a multi-cell EKF

The future evolution of technological systems dedicated to improve energy efficiency will strongly depend on effective and reliable Energy Storage Systems, as key components for Smart Grids, microgrids and electric mobility. Besides possible improvements in chemical materials and cells design, the Battery Management System is the most important electronic device that improves the reliability of a battery pack. In fact, a precise State of Charge (SoC) estimation allows the energy flows controller to exploit better the full capacity of each cell. In this paper, we propose an alternative definition for the SoC, explaining the rationales by a mechanical analogy. We introduce a novel cell model, conceived as a series of three electric dipoles, together with a procedure for parameters estimation relying only on voltage measures and a given current profile. The three dipoles represent the quasi-stationary, the dynamics and the istantaneous components of voltage measures. An Extended Kalman Filer (EKF) is adopted as a nonlinear state estimator. Moreover, we propose a multi-cell EKF system based on a round-robin approach to allow the same processing block to keep track of many cells at the same time. Performance tests with a prototype battery pack composed by 18 A123 cells connected in series show encouraging results.Comment: 8 page, 12 figures, 1 tabl

Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately predict wind generation. The Wind Power Prediction Tool (WPPT) has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs) model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed, but using LSTM blocks as units in the hidden layer. The training process of this network has two key stages: (i) the hidden layer is trained with a descending gradient method online using one epoch; (ii) the output layer is adjusted with a regularized regression. In particular, the case is proposed where Step (i) is used as a target for the input signal, in order to extract characteristics automatically as the autoencoder approach; and in the second stage (ii), a quantile regression is used in order to obtain a robust estimate of the expected target. The experimental results show that LSTM+ESN using the autoencoder and quantile regression outperforms the WPPT model in all global metrics used

イランにおける持続可能な社会に向けた移行計画のための水・再生可能エネルギーネクサスの研究

京都大学新制・課程博士博士(エネルギー科学)甲第23293号エネ博第418号新制||エネ||79(附属図書館)京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻(主査)教授 手塚 哲央, 准教授 MCLELLAN Benjamin, 教授 山敷 庸亮学位規則第4条第1項該当Doctor of Energy ScienceKyoto UniversityDFA