Iana Vassileva

Optimization of the Energy System to Achieve a National Balance Without Fossil Fuels

In this article, the overall energy balance for Sweden and to some extent EU27 is discussed. It deals with the reduction of the total consumption in industrial, transport, and domestic sectors through more efficient vehicles, industrial processes, and buildings and individual behavior. The conclusion is that it should be relatively easy for Sweden to reach a sustainable society if the political will, in the form of policies and incentives, is present. It would also be possible for the EU27 to reach a sustainable society, although it would be more demanding (challenging?).

Electricity demand impact from increased use of ground sourced heat pumps

The use of ground-sourced heat pumps as main heating systems has increased in Sweden in the last fifteen years to the point that it is the country with the highest amount of GSHP in Europe. Heat pumps are chosen by many households due to their economic savings value; In contrast, electricity prices in Sweden have almost doubled since 2006, threatening their economic benefits. It is therefore, essential to understand GSHPs impact on the users electricity consumption and provide suitable demand-response programs that could help develop a model capable of forecasting consumption and provide decision support information to make the best use of the technology. This paper analyses questionnaire surveys and consumption patterns were evaluated for 322 households with installed GSHPs and different pricing schemes in order to increase the understanding of mass use of this type of heating system.

A Smart Energy system: Distributed resource management, control and optimization

This paper presents a novel concept of distributed energy resource and consumption management, which proposes to design a networked and embedded platform for realizing a dynamic energy mix and optimizing the energy consumption dynamically. Based on heterogeneous wireless sensor networks and a local Web of Things platform, the environmental parameters and energy data can be acquired and processed in a distributed manner in real time. In order to improve understanding on how different environmental factors and user behaviors influence the end use of energy, we propose a User Profiling module to investigate the characterization of users goals and behaviors in terms of energy consumption. Besides the wireless sensor networks, the User Profiling module acquires data also from a questionnaire which mainly concerns four categories, i.e. characteristics of the residents, electrical appliances, attitudes towards energy and building structural information. Furthermore, based on the real-time information from the sensor network platform and the user profiling module, an embedded Resource and Consumption Controller will then adapt automatically for instance the regulation processes of energy consumption in a household locally for the users, so that the costs of all energy resources will not exceed the predetermined budget and be regulated in a user-preferred way.

An adaptive optimization model for power conservation in the smart grid

Dynamically adaptive systems (DAS) such as smart grids, cloud computing applications, sensor networks and P2P networks tend to change their structure at runtime. Therefore, design-time modeling for such systems are sometimes not enough to incorporate self-* properties. To this end, we have developed a dynamic mathematical modeling framework for runtime optimizations for DAS. In this paper, we describe how our system engineers a linear programming model by using a smart-grid application for power distribution as a case-study. At runtime whenever an optimization is desired this modeling framework captures the state of the system, converts it into an appropriate linear programming model, plan the changes using mathematical manipulations and apply the changes to the actual system. Our results show that this framework is able to capture accurate runtime models of large power systems and is able to adapt itself with the change in the size or structure of the system.

Engineering Optimization Models at Runtime for Dynamically Adaptive Systems

Dynamically adaptive systems (DAS), such as smart grids, cloud computing applications, sensor networks and P2P networks tend to change their structure at runtime. Therefore, design-time modeling for such systems are sometimes not enough for self-management. To this end, we have developed a dynamic mathematical modeling framework for runtime modeling for DAS. In this paper, we describe how our system engineers a linear programming model for self-optimization by using a smart-grid application for power distribution as a case-study. At runtime whenever, an optimization is desired this modeling framework captures the state of the system, converts it into an appropriate linear programming model, plan the changes using mathematical manipulations and apply the changes to the actual system. Our initial simulation results show that this framework is able to capture accurate runtime models of large power systems and is able to adapt itself with the change in the size or structure of the system by constructing a succinct model which is faster and more efficient than a design time model.

Perceptions and Adoption of EVs for Private Use and Policy Lessons Learned

Electric vehicles (EVs) are considered one of the most promising solutions to mitigate greenhouse gas (GHG) emissions produced in the transport sector. EVs have many potential advantages (e.g., in terms of avoided local and global pollutant emissions and noise reduction), but may also create new problems (e.g., in terms of stress on the electric distribution network or congested public transport lanes). The ultimate pollution emission benefit depends strongly on the fuel mix for electricity generation. Numerous governments at all levels worldwide have started to provide monetary and other incentives to render EVs more attractive for users, including research, development, and dissemination (RD&D) support, vehicle subsidies, provision of charging infrastructure, and privileged usage of bus lanes and dedicated parking lots. This chapter presents the different barriers explaining the slow market penetration of EVs so far, consumer perceptions and misconceptions, as well as lessons learned by policy makers and new empirical evidence and insights. Early adopter characteristics and selected examples where EV uptake has been particularly fast are also described. The conclusions show that subsidy and other incentive programs need to be carefully designed in scope, contents, and duration. In light of information deficiencies and misperceptions, information provision to potential EV adopters seems to be a no-regret policy option.

A Simulation Model of the Interactions between Power Producers and Customers

We have had a strong mechanism for interaction between power production companies and the power trading/supplier companies for a number of years by now. In the future we can expect new types of more interactive communications between single customers and groups of customers towards the energy market companies. We can expect a stronger request side from customers to buy only green energy, only nuclear etc.Power (kW) will be a part aside of energy (kWh) and there will be new possibilities to buy energy when it is as cheap as possible. This may include new applications like charging batteries for your car when the electricity price is low. Differentiation of price may be not only as a direct function of time, but also energy availability like when it is windy, as wind power becomes a major part of many energy systems. Energy storage will be more important and perhaps we will get new possibilities to buy shares in central energy storages like you buy space at servers for your web-pages etc.Other type of functions may develop as a result of the introduction of individual metering of first electricity and later on hot water and temperature. By metering the individual consumption and invoicing just what has been spent, you will have a driving force also to do actions like shutting of high demand functions like “infra-heating”, “large screen TV” and similar, when other usages are on, and the price is high. We expect displays with interaction possibilities in all homes, where you can see your consumption and pricing information.These new type of systems will put new demands on both hardware for supply and software to handle the services/functions. As part of developing this, mathematical modelling of the systems and tests with simulators will be an important tool. Also new soft ware functions will be developed to support the actual services, like simulators giving information on how different actions you make as a user will influence your energy consumption in the future, both short term and more long term. For the more long term case, new investments in new hardware and software may be proposed and evaluated for you in both technical and economic terms.In this paper the system aspects including the costs is analysed through a simulation model. This includes the physical system as well as the user behaviour and possible effects of different price models, like a combination of kW and kWh. The effect on the users, the distributors and the power producers are evaluated.