The development of a novel agent based long term domestic energy stock model

This research has developed a novel long term domestic energy stock model of owneroccupied dwellings in England. Its primary purpose is to aid policy makers in determining appropriate policy measures to achieve CO2 emissions reductions in the housing sector. Current modelling techniques can provide a highly disaggregated technology rich environment, but they do not consider the behaviour required for technological changes to the dwelling stock. Energy efficiency improvements will only occur in the owner-occupied sector of the housing market when owners decide to carry out such improvements. Therefore, a stock model that can simulate this decision making process will be of more use for policy makers in predicting the impact of different measures designed to encourage uptake of suitable technologies. Agent based modelling has been proposed as a solution to allow the inclusion of individual household decision making into a long term domestic stock model. The agents in the model represent households and have a simple additive weighting decision making algorithm based on discrete choice survey data from the Energy Saving Trust and Element Energy. The model has then been calibrated against historic technology diffusion data. Sixteen scenarios have been developed and tested in the model. The initial Business as Usual scenarios indicate that current policies are likely to fall well short of the 2050 80% emissions reduction target, although subsequent scenarios indicate that the target is achievable. The results also indicate that care is required when setting subsidy levels when competing technologies are available, as there is the potential to suppress the diffusion of technologies that offer greater potential savings. The developed model can now be used by policy makers in testing further scenarios, and this novel approach can be applied both regionally and in other countries, subject to the collection of suitable input data

Performance analysis of ground source heat pumps for buildings applications

Geothermal heat pumps (GSHPs), or direct expansion (OX) ground source heat pumps, are a highly efficient renewable energy technology, which uses the earth, groundwater or surface water as a heat source when operating in heating mode or as a heat sink when operating in a cooling mode. It is receiving increasing interest because of its potential to reduce primary energy consumption and thus reduce emissions of GHGs. The main concept of this technology is that it utilises the lower temperature of the ground (approximately <32°C), which remains relatively stable throughout the year, to provide space heating, cooling and domestic hot water inside the building area. The main goal of this study is to stimulate the uptake of the GSHPs. Recent attempts to stimulate alternative energy sources for heating and cooling of buildings has emphasised the utilisation of the ambient energy from ground source and other renewable energy sources. The purpose of this study, however, is to examine the means of reduction of energy consumption in buildings, identify GSHPs as an environmental friendly technology able to provide efficient utilisation of energy in the buildings sector, promote using GSHPs applications as an optimum means of heating and cooling, and to present typical applications and recent advances of GSHPs. The study highlighted the potential energy saving that could be achieved through the use of ground energy sources. It also focuses on the optimisation and improvement of the operation conditions of the heat cycle and performance of the GSHP. It is concluded that GSHP, combined with the ground heat exchanger in foundation piles and the seasonal thermal energy storage from solar thermal collectors, is extendable to more comprehensive applications

Essays on the Economics of Energy Markets - Security of Supply and Greenhouse Gas Abatement

In summary, the presented thesis analyzes two distinct economic subjects: security of supply in natural gas markets and greenhouse gas abatement potentials in the residential heating market. These subjects considered both reflect key points in the triangle of energy policy and are both associated with transnational market failures within energy markets. The security of supply analyses in an intermeshed network are approached from a rather normative, top-down perspective of a social planner. On the contrary, the analyses of greenhouse gases emitted by households are positive analyses of consumer choices. The normative analyses of security of supply in natural gas markets and the positive analyses on greenhouse gas abatement in the residential heating market are organized in two parts of the thesis. 1. Normative analyses - Security of supply in natural gas markets: The two papers of the first part of the dissertation thesis are based on a normative approach with the European natural gas market and infrastructure model TIGER that allows for security of supply analyses. The general idea behind the modeling approach is based on the assumption of a social planner and finds an efficient utilization of the natural gas infrastructure. More precisely, the security of supply analyses conducted in the first part of the thesis refer to scenario simulations of disrupted supply routes in the European natural gas network. The effects of these security of supply scenarios on the usage of other infrastructure components, on marginal supply costs and disruptions to consumers are investigated. 2. Positive analyses of greenhouse gas abatement potentials - Econometric modeling of consumer choices and evaluation of public policies: The second part of the thesis includes two positive analyses which investigate household choices to derive greenhouse gas abatement potentials. In the residential heating market, the energy efficiency level exhibited and the type of energy carrier used are determined by investment decisions and significantly affect the level of greenhouse gas emissions. Major investment decisions of households concern investments in heating systems and in dwelling insulation. The investment decision of heterogenous households is not strictly driven by monetary objectives but also by non-monetary preferences. Hence, understanding household behavior is crucial for the development of targeted policies in greenhouse gas abatement. In the third paper of the thesis, micro-economic greenhouse gas abatement curves are derived theoretically and numerically by applying the dynamic microsimulation model (DIscrHEat) for the residential heating market, which integrates a discrete choice estimation of household behavior by using data on actual heating choices. The last paper is a panel data analysis of the effectiveness of subsidies on residential investments in energy efficiency and on energy consumption applying a differences-in-differences-in-differences approach

Algorithms for energy management in micro-grids

Population explosion is one of the primary causes for concern in the power sector nowadays because residential buildings consume a high percentage of available electricity in the market. Also, the majority of current power plants use fossil fuel to generate electricity which makes the situation even worse due to the high price of fossil fuel. Consequently, electricity bills have soared dramatically in the last decade. If that was not enough, many countries have a shortage of electricity because they cannot increase their generation capacity to cover electricity demand. Many solutions have been introduced to improve the efficiency of the power grid and reduce electricity price for the users. For instance, Demand Side Management and Demand Response, domestic top-roof renewable micro-plants, and distributed renewable plants are introduced as a part of the solution to improve the situation. However, users are still paying a high percentage of their monthly income to electricity companies, that is because the surplus renewable power is not well utilized. The primary problem here is to find an efficient way to minimize the electricity cost and maximize the utilization of renewable power without using storage systems (batteries). Another issue is to solve the massive power allocation optimization problem in polynomial time. In this thesis, heuristic optimization algorithms are proposed to cope with the complexity of the problem as these kinds of problems are NP-hard. Furthermore, a set of different power allocation problems has been addressed in this thesis. The first one uses an online algorithm to solve power allocation problem that is modeled as a Knapsack problem. Additionally, the thesis has coped with the computational issue of a massive LP-based optimization problem of large buildings. Finally, an MILP-based heuristic algorithm has been used to solve power allocation problem in micro-grids (a set of houses shares renewable power for particulate rate). The empirical experiments and evaluations, in general, show promising results. The findings depict how an appropriate knapsack formulation can be used to address a significant dynamic energy allocation problem in a straightforward and flexible way and how good our heuristic algorithms can solve enormous power optimization problem in polynomial time. Finally, the results prove that our micro-grid model can reduce power bills by using the principle of renewable power sharing for a fair price

Environmental Technology Applications in the Retrofitting of Residential Buildings

The impact of buildings on the environment is nothing short of devastating. In recent years, much attention has been given to creating an environmentally friendly built environment. Nonetheless, it has been levied on new buildings. Residential buildings make up at least 80% of the built environment, most of which were built before any energy efficiency guidelines or regulations were introduced. Retrofitting existing residential buildings is a key yet neglected priority in effecting the transition to an environmentally friendly, sustainable built environment. It is pivotal to reducing a building’s energy consumption while simultaneously improving indoor environmental quality and minimizing harmful emissions. This Special Issue showcases studies investigating applications of environmental technology that is tailored to enhance the sustainable performance of existing residential buildings. It helps to better understand the innovations that have been taking place in retrofitting residential buildings, as well as highlighting many opportunities for future research in this field

Dynamic modeling and ICT integration for Demand Side Management (DSM) of systems for heating, cooling and related electrical loads

Nowadays the energy usage is increasing the urban areas due to lifestyle changes and an increase in the population of cities. Consumers care more about their comfort level which affects energy usage. Improvement of energy efficiency of cooling and heating systems of buildings is a suitable approach for energy consumption reduction of urban areas. In existing buildings, it is difficult to intervene on the building envelope. Therefore, an alternative solution is using a smart controller for heating and cooling systems of buildings to make the total system more efficient. In the current work, first, a general energy model is designed and developed to be implementable to different kinds of buildings. The model contains different elements including boiler, chiller, fan coil, radiator, pipe, heat exchanger, air heat exchanger, zone, mixer, solver, and bridge. Then, the model is implemented on the case study building based on the heating and cooling plants of that. The model is validated in terms of indoor temperature in heating and cooling systems and $CO_{2}$ concentration. Next, two different approaches are studied, one for an islanded building, and another for the connected buildings. For the islanded building, it is planned to just keep the thermal comfort and decrease the energy consumption. Therefore, in this case, the load shape in a neighborhood will not be considered. Different scenarios are designed to be compared in terms of energy consumption and thermal comfort, including the basic, the indoor temperature, the weather prediction, and the smart scenarios. The other approach, which is the modeling through a neighborhood, helps to decrease energy consumption and improve the power load shape in the neighborhood. Meanwhile, the thermal comfort will be kept in a suitable range. Different scenarios are designed to be compared in terms of energy consumption, power load shape, and thermal comfort, including the basic connected, the smart connected scenarios. As the results show, implementing smart solutions in both approaches, islanded and connected, can improve the energy consumptions of the existing buildings. For energy consumption of islanded approach in the heating system, the smart scenario is the most effective in terms of energy consumption, which can reduce the energy consumption compared to the basic scenario about 10.7%. In that of the cooling system, the smart scenario can save more energy compared to the other scenarios, which is 9.7%, compared to the basic scenario. In the connected approach, using a smart controller interacting through the blockchain decreases the PAR by 15% compared to that of basic, and it decreases total energy consumption by 11%. The smart scenario brings 7% more thermal comfort compared to the basic scenario

Dynamic Allocation of a Domestic Heating Task to Gas-Based and Heatpump-Based Heating Agents

In this paper a multi-agent model for a domestic heating task is introduced and analysed. The model includes two alternative heating agents (for gas-based heating and for heatpump-based heating), and a third allocation agent which determines the most economic allocation of the heating task to these heating agents over the days in a year. For allocation decisions it is analysed how the performance of a heatpump depends on the outdoor temperature. One method discussed is a what-if analysis method using agent-based simulation, another method is by mathematical analysis to derive more precise knowledge about the most optimal allocation choice. These methods can be used by the allocation agent to determine in a dynamic, adaptive manner per day a most economic allocation, depending on the (predicted) outdoor temperature

Selected Papers from SDEWES 2017: The 12th Conference on Sustainable Development of Energy, Water and Environment Systems

EU energy policy is more and more promoting a resilient, efficient and sustainable energy system. Several agreements have been signed in the last few months that set ambitious goals in terms of energy efficiency and emission reductions and to reduce the energy consumption in buildings. These actions are expected to fulfill the goals negotiated at the Paris Agreement in 2015. The successful development of this ambitious energy policy needs to be supported by scientific knowledge: a huge effort must be made in order to develop more efficient energy conversion technologies based both on renewables and fossil fuels. Similarly, researchers are also expected to work on the integration of conventional and novel systems, also taking into account the needs for the management of the novel energy systems in terms of energy storage and devices management. Therefore, a multi-disciplinary approach is required in order to achieve these goals. To ensure that the scientists belonging to the different disciplines are aware of the scientific progress in the other research areas, specific Conferences are periodically organized. One of the most popular conferences in this area is the Sustainable Development of Energy, Water and Environment Systems (SDEWES) Series Conference. The 12th Sustainable Development of Energy, Water and Environment Systems Conference was recently held in Dubrovnik, Croatia. The present Special Issue of Energies, specifically dedicated to the 12th SDEWES Conference, is focused on five main fields: energy policy and energy efficiency in smart energy systems, polygeneration and district heating, advanced combustion techniques and fuels, biomass and building efficiency