Estimating a threshold price for CO2 emissions of buildings to improve their energy performance level. Case study of a new Spanish home

Energy consumption in homes produces CO2. In many countries, building regulations are being set to enable energy efficiency performance levels to be issued. In Spain, there is a regulated procedure to certify the energy performance of buildings according to their CO2 emissions. Consequently, some software tools have been design to simulate buildings and to obtain their energy consumption and CO2 emissions. In this paper the investment, maintenance and energy consumption costs are calculated for different energy performance levels and for various climatic zones, in a single-family home. According to the results, more energy efficient buildings imply higher construction and maintenance costs, which are not compensated by lower energy costs. Therefore, under current conditions, economic criteria do not support the improvement of the energy efficiency of a dwelling. 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A Multi-Criteria decision analysis framework to determine the optimal combination of energy efficiency and indoor air quality schemes for English school classrooms

Maintaining good Indoor Environmental Quality (IEQ) in English schools in terms of overheating and air quality is important for the health and educational performance of children. Improving energy efficiency in school buildings is also a key part of UK’s carbon emissions reduction strategy. To address the trade-offs between energy efficiency and IEQ, a Multi–Criteria Decision Analysis (MCDA) framework based on an English classroom stock model was used. The aim was to determine robust optimal school building interventions across a set of criteria (including child health, educational attainment and building energy consumption) and settings (comprising different climate scenarios, construction eras, geographical regions and school geographical orientations). Each intervention was made up of the pairwise combination of an energy efficiency retrofit scheme and an IEQ improvement scheme. The MCDA framework was applied to the school building stock in England. This study shows that the framework represents a transparent approach to support decision making in determining the optimal school building intervention from different perspectives. The optimal interventions included measures that improved IEQ and resulting indoor learning environments, such as external shading, or increased albedo and internal blinds, for the particular set of interventions, criteria and stakeholders in this study. The results of the MCDA analysis were sensitive to the preferences elicited from stakeholders on the relative importance of the criteria and to the range of interventions and criteria selected for evaluation

Energy Management of Distributed Generation Systems

The book contains 10 chapters, and it is divided into four sections. The first section includes three chapters, providing an overview of Energy Management of Distributed Systems. It outlines typical concepts, such as Demand-Side Management, Demand Response, Distributed, and Hierarchical Control for Smart Micro-Grids. The second section contains three chapters and presents different control algorithms, software architectures, and simulation tools dedicated to Energy Management Systems. In the third section, the importance and the role of energy storage technology in a Distribution System, describing and comparing different types of energy storage systems, is shown. The fourth section shows how to identify and address potential threats for a Home Energy Management System. Finally, the fifth section discusses about Economical Optimization of Operational Cost for Micro-Grids, pointing out the effect of renewable energy sources, active loads, and energy storage systems on economic operation

Multi-objective Smart Charge Control of Electric Vehicles

With the increasing integration of electric vehicles and renewable energy sources in electricity networks, key opportunities in terms of a cleaner environment and a sustainable energy portfolio are unlocked. However, the widespread deployment of these two technologies, can entail significant challenges for the electricity grid and in a larger context for the society, when they are not optimally integrated. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are being proposed as crucial solutions to achieve economic, technical and environmental benefits in future smart grids. The implementation of these technologies involves a number of key stakeholders, namely, the end-electricity user, the electric vehicle owner, the system operators and policy makers. For a wider and efficient implementation of the smart grid vision, these stakeholders must be engaged and their aims must be fulfilled. However, the financial, technical and environmental objectives of these stakeholders are often conflicting, which leads to an intricate paradigm requiring efficient and fair policies. With this focus in mind, the present research work develops multi-objective optimisation algorithms to control the charging and discharging process of electric vehicles. Decentralised, hybrid and real-time optimisation algorithms are proposed, modelled, simulated and validated. End user energy cost, battery degradation, grid interaction and CO2 emissions are optimised in this work and their trade-offs are highlighted. Multi-criteria-decision-making approaches and game theoretical frameworks are developed to conciliate the interests of the involved stakeholders. The results, in the form of optimal electric vehicle charging/discharging schedules, show improvements along all the objectives while complying with the user requirements. The outcome of the present research work serves as a benchmark for informing system operators and policy makers on the necessary measures to ensure an efficient and sustainable implementation of electro-mobility as a fundamental part of current and future smart grids

Policy-based power consumption management in smart energy community using single agent and multi agent Q learning algorithms

Power consumption in residential sector has increased due to growing population, economic growth, invention of many electrical appliances and therefore is becoming a growing concern in the power industry. Managing power consumption in residential sector without sacrificing user comfort has become one of the main research areas recently. The complexity of the power system keeps growing due to the penetration of alternative sources of electric energy such as solar plant, Hydro, Biomass, Geothermal and wind farm to meet the growing demand for electricity. To overcome the challenges due to complexity, the power grid needs to be intelligent in all aspects. As the grid gets smarter and smarter, considerable efforts are being undertaken to make the houses and businesses smarter in consuming the electrical energy to minimize and level the electricity demand which is also known as Demand Side Management (DSM). It also necessitates that the conventional way of modelling, control and energy management in all sectors needs to be enhanced or replaced by intelligent information processing techniques. In our research work, it has been done in several stages. (Purpose of Study and Results) We proposed a policy-based framework which allows intelligent and flexible energy management of home appliances in a smart home which is complex and dynamic in ways that saves energy automatically. We considered the challenges in formalizing the behaviour of the appliances using their states and managing the energy consumption using policies. Policies are rules which are created and edited by a house agent to deal with situations or power problems that are likely to occur. Each time the power problem arises the house agent will refer to policy and one or a set of rules will be executed to overcome that situation. Our policy-based smart home can manage energy efficiently and can significantly participate in reducing peak energy demand (thereby may reduce carbon emission). Our proposed policy-based framework achieves peak shaving so that power consumption adapts to available power, while ensuring the comfort level of the inhabitants and taking device characteristics in to account. Our simulation results on MATLAB indicate that the proposed Policy driven homes can effectively contribute to Demand side power management by decreasing the peak hour usage of the appliances and can efficiently manage energy in a smart home in a user-friendly way. We propounded and developed peak demand management algorithms for a Smart Energy Community using different types of coordination mechanisms for coordination of multiple house agents working in the same environment. These algorithms use centralized model, decentralized model, hybrid model and Pareto resource allocation model for resource allocation. We modelled user comfort for the appliance based on user preference, the power reduction capability and the important activities that run around the house associated with that appliance. Moreover, we compared these algorithms with respect to their peak reduction capability, overall comfort of the community, simplicity of the algorithm and community involvement and finally able to find the best performing algorithm among them. Our simulation results show that the proposed coordination algorithms can effectively reduce peak demand while maintaining user comfort. With the help of our proposed algorithms, the demand for electricity of a smart community can be managed intelligently and sustainably. This work is not only aiming for peak reduction management it aims for achieving it while keeping the comfort level of the inhabitants is minimum. It can learn user’s behaviour and establish the set of optimal rules dynamically. If the available power to a house is kept at a certain level the house agent will learn to use this notional power to operate all the appliances according to the requirements and comfort level of the household. This way the consumers are forced to use the power below the set level which can result in the over-all power consumption be maintained at a certain rate or level which means sustainability is possible or depletion of natural resources for electricity can be reduced. Temporal interactions of Energy Demand by local users and renewable energy sources can also be done more efficiently by having a set of new policy rules to switch between the utility and the renewable source of energy but it is beyond the scope of this thesis. We applied Q learning techniques to a home energy management agent where the agent learns to find the optimal sequence of turning off appliances so that the appliances with higher priority will not be switched off during peak demand period or power consumption management. The policy-based home energy management determines the optimal policy at every instant dynamically by learning through the interaction with the environment using one of the reinforcement learning approaches called Q-learning. The Q-learning home power consumption problem formulation consisting of state space, actions and reward function is presented. The implications of these simulation results are that the proposed Q- learning based power consumption management is very effective and enables the users to have minimum discomfort during participation in peak demand management or at the time when power consumption management is essential when the available power is rationale. This work is extended to a group of 10 houses and three multi agent Q- learning algorithms are proposed and developed for improving the individual and community comfort while at the same time keeping the power consumption below the available power level or electricity price below the set price. The proposed algorithms are weighted strategy sharing algorithm, concurrent Q learning algorithm and cooperative distributive learning algorithm. These proposed algorithms are coded and tested for managing power consumption of a group of 10 houses and the performance of all three algorithms with respect to power management and community comfort is studied and compared. Actual power consumption of a community and modified power consumption curves using Weighted Strategy Sharing algorithm, Concurrent learning and Distributive Q Learning and user comfort results are presented, and the results are analysed in this thesis

Combined power system planning and policy proposition for future electric vehicle charging infrastructure

This thesis was submitted for the award of Master of Philosophy and was awarded by Brunel University LondonIn order to achieve the UK Government’s legally bound framework of greenhouse gas reduction targets, the transport sector is undergoing drastic changes. The key action taken by the Department of Transport in addressing the issue was the introduction of Ultra-low emission vehicles (ULEV) concept. Office for Low Emission Vehicles (OLEV) was introduced to support early market for ULEV and development of efficient recharging network through Plugged-in Places programme. The massive deployment of EV charging stations across GB will have direct impact on GB power system as they require electricity supply for their operation. It is therefore deemed necessary to carry out investigations on the capacity of the network assets to handle this load and to develop policies to manage the future EV charging infrastructure efficiently. This thesis provides an overview of the EV technology introducing various technicalities behind EVs and the associated charging stations. The extended theory about interoperability between EVs and power networks is also presented. Investigation of an 11kV networked site and 66/11 kV networked area is performed to determine their potential in accommodating future EV charging infrastructure. A methodology has been proposed to carry out investigations in 11kV networked site. For analysis purpose both the real networks are modelled in detail using power system analysis software Electrical Transient Analyzer Programme (ETAP). Scottish and Southern Energy (SSE) and Northern Power Grid (NPG) are the owners of the distribution networks respectively. Collaboration with DNOs has taken place to collect the existing network data. Finally, a university based EV charging bays management policy has been proposed

Assessment of applications of optimisation to building design and energy modelling

Buildings account for around 35% of the world’s carbon emissions and strategies to reduce carbon emissions have made much use of building energy modelling. Optimisation techniques promise new ways of achieving the most cost effective and efficient solutions more quickly and with less input from engineers and building physicists. However, there is limited research into the practical applications of these techniques to building design practice. This thesis presents the results of case-based research into the practical application of design stage optimisation and calibration methods to energy efficient building fabric and services design using building energy modelling. The application during early stage design of a Non-dominating Sorting Genetic Algorithm 2 (NSGA2) to a building energy model EnergyPlusTM. The exercise was used to determine if the application of NSGA2 yielded a significant improvement in the selection of building services technology and building fabric elements. The use of NSGA2 enabled significant (£400,000) capital cost savings without degrading the comfort or energy performance. The potential capital cost savings significantly outweighed the cost of the engineering time required to carry out the additional analysis. Three optimisation techniques were applied to three case study buildings to select appropriate model parameters to minimise the difference between modelled and measured parameters and hence calibrate the model. An heuristic approach was applied to the Institute for Life Sciences Building 1 (ILS1) at Swansea University. Latin Hypercube Monte Carlo (LHMC) was applied to the Arup building at 8 Fitzroy St London and compared directly with the results from an approach using Self Adaptive Differential Evolution (SADE). Poor Building Management System data quality was found to significantly limit the potential to calibrate models. Where robust data was available it was however found to be possible to calibrate EnergyPlus simulations of complex real world buildings using LHMC and SADE methods at levels close to that required by professional bodies

The development of object oriented Bayesian networks to evaluate the social, economic and environmental impacts of solar PV

Domestic and community low carbon technologies are widely heralded as valuable means for delivering sustainability outcomes in the form of social, economic and environmental (SEE) policy objectives. To accelerate their diffusion they have benefited from a significant number and variety of subsidies worldwide. Considerable aleatory and epistemic uncertainties exist, however, both with regard to their net energy contribution and their SEE impacts. Furthermore the socio-economic contexts themselves exhibit enormous variability, and commensurate uncertainties in their parameterisation. This represents a significant risk for policy makers and technology adopters. This work describes an approach to these problems using Bayesian Network models. These are utilised to integrate extant knowledge from a variety of disciplines to quantify SEE impacts and endogenise uncertainties. A large-scale Object Oriented Bayesian network has been developed to model the specific case of solar photovoltaics (PV) installed on UK domestic roofs. Three specific model components have been developed. The PV component characterises the yield of UK systems, the building energy component characterises the energy consumption of the dwellings and their occupants and a third component characterises the building stock in four English urban communities. Three representative SEE indicators, fuel affordability, carbon emission reduction and discounted cash flow are integrated and used to test the model s ability to yield meaningful outputs in response to varying inputs. The variability in the percentage of the three indicators is highly responsive to the dwellings built form, age and orientation, but is not just due to building and solar physics but also to socio-economic factors. The model can accept observations or evidence in order to create scenarios which facilitate deliberative decision making. The BN methodology contributes to the synthesis of new knowledge from extant knowledge located between disciplines . As well as insights into the impacts of high PV penetration, an epistemic contribution has been made to transdisciplinary building energy modelling which can be replicated with a variety of low carbon interventions

Scheduling and Sizing of Campus Microgrid Considering Demand Response and Economic Analysis

Current energy systems face multiple problems related to inflation in energy prices, reduction of fossil fuels, and greenhouse gas emissions which are disturbing the comfort zone of energy consumers and the affordability of power for large commercial customers. These kinds of problems can be alleviated with the help of optimal planning of demand response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of the green distributed generators (DGs). To this end, a real-time based campus is considered that currently takes provision of its loads from the utility grid only. According to the proposed given scenario, it will contain solar panels and a wind turbine as non-dispatchable DGs while a diesel generator is considered as a dispatchable DG. It also incorporates an energy storage system with optimal sizing of BESS to tackle the multiple disturbances that arise from solar radiation. The resultant problem of linear mathematics was simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of energy management (EMS) minimizes the grid electricity costs by 668.8 CC/day ($) which is 36.6% of savings for the campus microgrid. The economic prognosis for the campus to give an optimum result for the UET Taxila, Campus was also analyzed. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs was also determined. The substantial environmental and economic benefits compared to the present situation have prompted the campus owners to invest in the DGs and to install large-scale energy storage

Optimising Energy Systems in Smart Urban Areas

In this chapter, the urban structure will be defined with zero or almost zero energy consumption, followed by pollution parameters. Energy systems are designed as networks of energy-intensive local hubs with multiple sources of hybrid energies, where different energy flows are collected on the same busbar and can be accumulated, delivered, or transformed as needed into the intelligent urban area. For analysis of the purpose function of our energy system, a micro-network of renewable energy sources (RES) is defined by penalization and limitations. By using fuzzy logic, a set of permissible solutions of this purpose function is accepted, and the type of daily electricity consumption diagrams is defined when applying cluster analysis. A self-organising neural network and then a Kohonen network were used. The experiment is to justify the application of new procedures of mathematical and informatics-oriented methods and optimisation procedures, with an outlined methodology for the design of smart areas and buildings with near zero to zero energy power consumption