Flexibility options and their representation in open energy modelling tools

To reach climate targets, future energy systems must rely heavily on variable renewable energy sources (VRES) such as wind and photovoltaic (PV). As the share of VRES increases, the topics of flexibility and the smart interplay of different flexibility options grow in importance. One way to analyse flexibility options and enhance the design of future energy systems is to use energy system modelling tools. Although a wide range of openly accessible models exist, there is no clear evaluation of how flexibility is represented in these tools. To bridge this gap, this paper extracts the key factors of flexibility representation and introduces a new classification for flexibility and influencing factors. To evaluate the current modelling landscape, a survey was sent to developers of open energy modelling tools and analysed with the newly introduced Open ESM Flexibility Evaluation Tool (OpFEl), an open source evaluation algorithm to assess the representation of different flexibility options in the tools. The results show a wide range of different tools covering most aspects of flexibility. A trend towards including sector coupling elements is visible. However, storage and network type flexibility, as well as aspects touching system operations, are still underrepresented in current models and should be included in more detail. No single model covers all categories of flexibility options to a high degree, but a combination of different models through soft coupling could serve as the basis for a holistic flexibility assessment. This, in turn, would allow for a detailed evaluation of energy systems based on VRES.DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

A Smart Grid Approach to Sustainable Power System Integration

Many factors can be identified for faster incorporation of renewable energy resources to displace the traditional fossil fuel energy sources. These factors are divided into three different aspects. First is the rapid decline of the cost of renewable energy sources and their associated components. The second factor can be attributed to the increasing pressure to transition from fossil-fuel energy sources which have detrimental environmental effects towards more sustainable energy source. A third aspect can be introduced in countries which are blessed with an enormous amount of fossil fuel resources, where the preservation of these limited natural resources is of paramount importance to the country that holds it. The dissertation includes the Kingdom of Saudi Arabia as the primary case study. However, the algorithm developed is applicable for other geographical locations which share similarities to the kingdom. The kingdom is considered to be one of the countries with an abundance of fossil-fuel reserves. The unique features of Saudi Arabia are primarily the availability of solar radiation and wind speed as well as high percentage of electrical loads which can be controlled such as energy-intensive desalination plants. This feature, in particular, provides a significant driver for renewables to penetrate the electricity generation mixture. With loads that are deferrable, the issue of renewable sources variability can be mitigated and reduced with an optimized operation strategy. Therefore, the research tends to define and model electrical loads by how susceptible they are to the time of service. The types of loads considered are summarized as non-deferrable such as typical electrical loads in which the demand must be satisfied instantly, semi-deferrable loads which they share the same features as the non-deferrable, however, a storage medium is available to store energy products for later usage. This category of loads is represented by a water desalination plant with a water tank storage. The final load model is the fully deferrable load which is flexible in regarding time of service, and this type of load can be represented by an industrial production factory, such as a steel or aluminum plants. The concept of value storage is introduced, where energy can be stored in different forms which are quite different from a typical storage component (i.e., batteries). The justification to start increasing the penetration of renewable sources into the existing grid in countries which have abundant fossil fuel might not be evident. However, the dissertation provides both economical as well as environmental justifications and incentives to approach more sustainable energy sources. The economical and technical evaluation is referred to as the Generation Expansion Planning (GEP). This type of problem is associated with high complexity and non-linearity. Therefore, computational intelligence based optimization methods are used to resolve these issues. Heuristic optimization methodologies are utilized to solve the developed problem which provides a fixable approach to solve optimization problems

Large-Scale Demand Management in Smart Grid

Future energy grids are expected to rely extensively on controlling consumers' demands to achieve an efficient system operation. The demand-side of the power network is usually constituted of a large number of low power loads, unlike energy production which is concentrated in a few numbers of high power generators. This research is concerned with supporting the management of numerous loads, which can be challenging from a computational point-of-view. A common approach to facilitate the management of a large number of resources is through resource aggregation (clustering). Therefore, the main objective of our research is to develop efficient load aggregation methodologies for two categories of demands: residential appliances and electric vehicles. The proposed methodologies are based on queueing theory, where each queue represents a certain category (class) of demand. Residential appliances are considered in the context of two demand management problems, where the first aims to minimize the energy consumption cost, while the second aims to reduce the magnitude of fluctuations in net demand, as a result of a large-scale integration of renewable energy sources (RESs). Existing models for residential demand aggregation suffer from two limitations:first, demand models ignore the inter-temporal demand dependence that is induced by scheduling deferrable appliances; Second, aggregated demand models for thermostatically-controlled loads are computationally inefficient to be used in DR problems that require optimization over multiple time intervals. Although the same aggregation methodology is applied to both problems, each one of them requires a different demand scheduling algorithm, due to the stochastic nature of RESs which is introduced in the second problem. The second part of our research focuses on minimizing the expected system time needed for charging electric vehicles (EVs). This target can be achieved by two types of decisions, the assignment of EVs to charging stations and the charging of EVs' batteries. While there exist aggregation models for batteries' charging, aggregation models for EVs' assignment are almost non-existent. In addition, aggregation models for batteries' charging assume that information about EVs' arrival times, departure times and their required charging energies are given in advance. Such assumption is non-realistic for a charging station, where vehicles arrive randomly. Hence, the third problem is concerned with developing an aggregation model for EVs' assignment and charging, while considering the stochastic nature of EVs' arrivals. Realistic models for residential demands and RES powers were used to develop the corresponding numerical results. The proposed scheduling algorithms do not require highly restrictive assumptions. The results proved that effectiveness of the proposed methodology and algorithms in achieving a significant improvement in the problems' objectives. On the other hand, the algorithm used in EV assignment requires restrictive Markovian assumptions. Hence, we needed to verify our proposed analytical model with a more realistic simulation model. The results showed a good compliance between both models. Our proposed methodology helped in improving the average system time significantly, compared to that of a near-station-assignment policy. This study is expected to have an important contribution from both research and application perspectives. From the research side, it will provide a tool for managing a large, diverse number of electric appliances by classifying them according to how much they can benefit the utility. From the application side, our work will help to include residential consumers in demand response (while current DR programs focus on the industrial sector only). It will also facilitate RESs and EVs on a large scale to help address environmental concerns

Appliance Classification and Scheduling in Residential Environments with Limited Data and Reduced Intrusiveness

The United Kingdom aims for a 78% reduction in greenhouse gas emissions by 2035, with a specific carbon budget for 2033–2037. Despite rising CO2 emissions from 2021 to 2022 due to increased energy demands, this thesis presents novel strategies to reduce residential electricity consumption, a major emissions driver. It addresses two critical gaps in energy management: First, it develops a feature extraction methodology using machine learning and deep learning for accurately classifying high-power household appliances with smart meter data. Traditional methods often require complex setups or large datasets, leading to intrusiveness and implementation challenges. This research introduces the Spectral Entropy – Instantaneous Frequency (SE-IF) method, effective with limited datasets and enhancing usability (Chapter 3). Second, it proposes an optimisation model that intelligently schedules household appliance usage to balance costs, emissions, and user comfort, incorporating renewable energy and battery storage systems. Existing scheduling techniques typically overlook significant CO2 reductions and user comfort. The thesis utilises the Multiobjective Immune Algorithm (MOIA) to demonstrate this model’s effectiveness, achieving a 9.67% cost reduction and a 16.58% decrease in emissions (Chapter 5). Chapters 4 and 5 further detail how the SE-IF method, paired with a Bidirectional Long Short-Term Memory (BiLSTM) network, achieves a 94% accuracy in identifying appliances from aggregated data and applies the multi-objective optimisation in various scenarios. This research advances the integration of energy efficiency, environmental sustainability, and user-centric solutions in smart homes, contributing significantly to national goals of reducing energy consumption and emissions

Proceedings of the International Conference on Energising the SDGs through Appropriate Technology and Governance

This volume presents the papers presented at the international conference on Energising the SDGs through appropriate technology and governance. Papers were presented in eight sessions. In addition, there was a keynote speech, a panel discussion, a workshop on Sustainability Compass and a lunch-time poster session. This compendium provides a summary of the event and includes original papers and posters delivered at the conference. These covered various themes, including climate action plan in UK and Japanese cities and their alignment with the SDGs; sustainable energy access; contribution of renewable energies, urban design and sustainable development goals, tools for evaluation and monitoring of progress with the SDGs, and innovations and business models for various services

Investigating Preconditions for Sustainable Renewable Energy Product–Service Systems in Retail Electricity Markets

Energy transitions are complex and involve interrelated changes in the socio-technical dimensions of society. One major barrier to renewable energy transitions is lock-in from the incumbent socio-technical regime. This study evaluates Energy Product–Service Systems (EPSS) as a renewable energy market mechanism. EPSS offer electricity service performance instead of energy products and appliances for household consumers. Through consumers buying the service, the provider company is enabled to choose, manage and control electrical appliances for best-matched service delivery. Given the heterogenous market players and future uncertainties, this study aims to identify the necessary conditions to achieve a sustainable renewable energy market. Simulation-Based Design for EPSS framework is implemented to assess various hypothetical market conditions’ impact on market efficiency in the short term and long term. The results reveal the specific market characteristics that have a higher chance of causing unexpected results. Ultimately, this paper demonstrates the advantage of implementing Simulation-Based Design for EPSS to design retail electricity markets for renewable energy under competing market mechanisms with heterogenous economic agents

Accomplishing rural electrification for over a billion people: Approaches towards sustainable solutions

Access to electricity appears to be a prerequisite to materialize social, economic, and human development in the underprivileged rural areas. However, 1.1 billion rural people in the world, almost all of them living in developing countries, still do not have access to electricity. Although the rural electrification process poses more challenges than urban electrification, rural areas are blessed with abundant and relatively evenly distributed renewable energy resources. To facilitate electricity access to this huge population, it is essential to deal with the rural electrification task by considering its challenging features and the potential merits of renewable resources. The objective of this thesis is to present policy and techno-economic frameworks for sustainable and accelerated rural electrification for over a billion people in developing countries. This thesis considers grid expansion as the primary option for rural electrification, and renewable resource based off-grid options were considered as the alternative where grid expansion is not feasible. Grid-based rural electrification policies were examined by focusing on one case program (the Bangladesh rural electrification program) in light of challenges that are generic for developing countries. The assessment of the potentials and techno-economic viability of renewable resources were performed by utilizing analytical methodologies and well-established computer tools (HOMER and RETScreen). The evaluation of choices among rural electrification alternatives has been illustrated with the help of the Stochastic Multicriteria Acceptability Analysis (SMAA) tool. The evaluation methods and tools are illustrated by employing case data obtained mainly from Bangladesh. This thesis observed that some key policy elements influence the performance of a grid-based rural electrification program. These policy elements guide the rural electrification program towards success through addressing distinct rural electrification challenges. Agricultural residues have the potential to generate electricity to meet household-level demands in rural areas of many developing countries. Hybrid biogas and solar resources can serve both clean-cooking and electricity loads in rural households with achieving benefit (saving) more than the cost. The multicriteria decision support technique enables a rural electrification program to choose decision options from different alternatives based on sustainability criteria.

High renewable energy penetration hybrid power system for rural and desert areas

This thesis proposes innovative ways of designing and controlling a small to medium size islanded or utility grid connected power system consisting of diesel generators, renewable energy sources and battery energy storages such that both fossil fuel usage and size of expensive battery bank can be minimized and the level of penetration of renewable energy can be raised to unprecedented levels. Computer software simulations and experimental results verify the proposed design and control strategies

Modelling and design of local energy systems incorporating heat pumps, thermal storage, future tariffs, and model predictive control

The planning-level design of local energy systems requires sufficiently capable modelling tools which incorporate heat pumps, thermal storage, future electricity markets, and predictive control strategies. Gaps were identified in a review of existing local energy system tools: (i) ability to adapt and access source code; (ii) temperature dependence for heat pump models; (iii) stratification model for thermal storage models; (iv) modelling of evolving electricity markets; and (v) ability to explore predictive controls. A novel modelling tool, PyLESA, has been developed to tackle these gaps and to explore predictive and non-predictive controls, and existing and future electricity tariffs. PyLESA possesses the following modelling capabilities: resources, and electrical and heat demands; electricity production; heat pump; hot water tank; electricity tariffs; fixed order control (FOC); model predictive control (MPC); and KPIs. A sizing study for a proposed design of a district heating network was devised to showcase an application of PyLESA. Aims were to compare control strategies and electricity tariffs, and to identify an optimal size combination of heat pump and hot water tank. Comparisons between control strategies found that MPC offers savings over FOC. The lowest levelized cost of heat for the existing electricity tariffs was for the time-of-use tariff with MPC, 750kW heat pump and 500m3 hot water tank. A wind tariff, with a 1000kW heat pump and 2000m3 hot water tank, benefits from using MPC over the FOC: levelized heat costs reduce by 41.1%, and heat demand met by RES increases from 52.8% to 70.2%. It is shown that the proposed design can be sized using existing electricity tariffs, and additional hot water tank capacity added later to benefit from future tariffs. The results convey the advantage of combining flexible tariffs with optimally sized thermal storage and showcase PyLESA as capable of usefully aiding the design of local energy systems.The planning-level design of local energy systems requires sufficiently capable modelling tools which incorporate heat pumps, thermal storage, future electricity markets, and predictive control strategies. Gaps were identified in a review of existing local energy system tools: (i) ability to adapt and access source code; (ii) temperature dependence for heat pump models; (iii) stratification model for thermal storage models; (iv) modelling of evolving electricity markets; and (v) ability to explore predictive controls. A novel modelling tool, PyLESA, has been developed to tackle these gaps and to explore predictive and non-predictive controls, and existing and future electricity tariffs. PyLESA possesses the following modelling capabilities: resources, and electrical and heat demands; electricity production; heat pump; hot water tank; electricity tariffs; fixed order control (FOC); model predictive control (MPC); and KPIs. A sizing study for a proposed design of a district heating network was devised to showcase an application of PyLESA. Aims were to compare control strategies and electricity tariffs, and to identify an optimal size combination of heat pump and hot water tank. Comparisons between control strategies found that MPC offers savings over FOC. The lowest levelized cost of heat for the existing electricity tariffs was for the time-of-use tariff with MPC, 750kW heat pump and 500m3 hot water tank. A wind tariff, with a 1000kW heat pump and 2000m3 hot water tank, benefits from using MPC over the FOC: levelized heat costs reduce by 41.1%, and heat demand met by RES increases from 52.8% to 70.2%. It is shown that the proposed design can be sized using existing electricity tariffs, and additional hot water tank capacity added later to benefit from future tariffs. The results convey the advantage of combining flexible tariffs with optimally sized thermal storage and showcase PyLESA as capable of usefully aiding the design of local energy systems