| Sažetak | Elektroenergetski sustav prolazi kroz sve veće promjene potaknute distribuiranim izvorima energije te električnim vozilima. Prodorom ICT tehnologija, pred mrežom se postavlja zahtjev za dvosmjernim tokom energije i informacija. Stohastička priroda obnovljivih izvora energije, zahtjeva osiguranje sustava za spremanje energije. Kako električna vozila već sadrže spremnike energije, omogućit će se pružanje pomoćnih usluga elektroenergetskom sustavu. Cilj istraživanja je razvoj optimizacijskog modela mikromreže temeljenog na integraciji obnovljivih izvora energije, električnih vozila kao potencijalnih spremnika energija i upravljanja potrošnjom kako bi se ovisnost o elektroenergetskom sustavu smanjila. Kvantitativne i kvalitativne metode anketiranja, odnosno metoda anketnog upitnika i metoda fokus grupa, koriste se za postavljanje profila (klaster) budućih potrošača, analiziranje njihovih navika vezanih uz potrošnju u kućanstvu, mobilnost, principa održivog razvoja te voljnosti da postanu aktivni potrošači. Algoritmi sustava upravljanja potražnjom temelje se na tehnici cjelobrojnog linearnog programiranja (eng. integer linear programing) i teoriji igara (eng. game theory). Optimizacija modela se provodi u programskom paketu GAMS. S obzirom na empirijske rezultate anketnog upitnika, može se zaključiti kako socio-ekonomski čimbenici ne utječu na ponašanje potrošača na tržištu električnom energijom, dok financijski poticaji imaju pozitivni učinak na ponašanje potrošača. Istraživanje metodom fokus grupa pokazalo je kako su sudionici pozitivno reagirali na predstavljene tehnologije naprede mreže. Većina ispitanika pristala bi na korištenje sustava automatizacije kućanskih trošila, no za upotrebu tehnologija napredne mreže, potrošače je potrebno educirati te omogućiti jednostavno korištenje. U radu je predložena kategorizacija trošila na nepomična trošila, trošila s pomakom snage i trošila s vremenskim pomakom. Optimizacijskim pristupom kategorizaciji trošila u kućanstvu, u odnosu na bazni model A, ovisnost o električnoj mreži je manja za model B 2,4 puta, za model C 2,5 puta, a za model D 4,8 puta. Energija proizvedena u mikromreži, u modelu A ima najveći udio od 75 % šalje se u mrežu, za razliku od modela B gdje je to manje za 3 %, modela C 2 %, a modela D 10 %. Iz ovoga slijedi da se u modelu D iskorištava 15 % više energije proizvedene u mikromreži. Tijekom noćnih perioda, električna vozila sudjeluju u izmjeni energije s mrežom i u iznosu od 10 % proizvodnje u mikromreži. U modelu A ovisnost je gotovo 50 % instalirane snage, u modelu B ovisnost o mreži iznosi gotovo 32 % instalirane snage. U modelu C i D, ovisnost o mreži ne prelazi 9 % instalirane snage. Modeli C i D smanjuju ovisnost o mreži s obzirom na model B za 3,7 puta, dok za model A to iznosi 5,8 puta. |
| Sažetak (engleski) | The term “smart grid” generally refers to the larger grid that integrates microgrids. A microgrid is a model for making a generation of electricity from resources, placed close to end-users making them competitive with central generation. The first power plants in the late 19th century were microgrids, so actually they have a long history. Microgrids enable the production and storage of renewable energy, as well as the exchange of electricity, between energy providers and consumers, to take place locally. Consumers are small-scale co-providers of energy reducing their dependence on the public grid. The social acceptance has to be ensured, providing them with an option to be more self-sufficient by becoming co-producers of electricity and forming their demand in the household. The residential sector is the major contributor to the global energy balance, so efficient demand management mechanisms are promising technique and proactive method to make users energy-efficient in the long term. Correspondingly, the attitudes and behaviours of energy consumers need to be modified, taking account of physical, social, cultural and institutional context, that shape and constrain people’s choices [1], [2]. Broad social acceptance issues largely determine the implementation of the smart grid. Today’s focus is more on the technology than on general social acceptance issues. Consequently, the smart grid is a sociotechnical network, characterized by the active management of both information and energy flows, to balance energy supply and demand. Replacing conventional consumer–producer relationship with multiple relationships, where the consumer is co-producing and supplying for partners in the microgrid, as a distributed generation and vice versa. Other relationships, “consumer to utility”, “consumer to grid manager” and “consumer to partners”, are also changing. However, these relationships are not supported by existing institutions in energy provision. Accordingly, the attitudes and behaviours in the context of energy in microgrids need to change fundamentally, since it is the combination of new scientific and technical, as well as socioeconomic and organisation components [3]. Scientific research reveals that this field is enormous and has a potential to be applied in the area of energy and environmental issues. In the field of domestic energy consumption, a significant influence on consumer behaviour includes disciplines such as sociology, social psychology, human geography and anthropology, as well as a lack of information. The behaviour of energy consumers is influenced in complex ways by factors such as price, awareness, trust and commitment, including a sense of moral obligation [4]. The relationship between socioeconomic development and energy consumption is bi-directional. The availability of electricity allows the application of modern technologies, which improve the productivity and increase economic welfare. Therefore, the increase in electricity consumption is the consequence of economic growth and development. The energy policy, the delivery price and available technology all affect individual energy consumption. The energy policy usually has an influence on the human behaviour in an institutional and impersonal form. The tariff policies represent consumer expenses and modification, which are assimilated by population in time. However, if a consumer is used to technology, it is not comfortable in changing equipment, especially if facing higher cost. The sociotechnical transition towards smart grids is an on-going process which requires adaptive behaviour by consumers, so qualitative methods were used to explore characteristics of energy. In this respect, it is essential to understand and involve consumers to successfully assume their new role as active participants in the electric power system. For energy providers, it is critical to developing a closer relationship with their consumers during the new service development process to ensure excellent performance of new services [5]. Many studies have recently published results where consumers have been involved in interviews and surveys to assess their perceptions, understanding and willingness to pay for the development of smart grid technologies. These studies acknowledge a positive consumer attitude towards smart grid technologies. However, they also recognise the need to address beliefs and misunderstandings that new technologies are not reliable, transparent and do not provide feedback [6]. Even acknowledged by the European commission, a lack of consumer confidence or choice in the new systems will fail to capture all of the potential benefits of the smart grid, building a social roadmap for the smart grid [7]. As argued by [8], the only aspect of the smart grid that can be smart are the people within it, so if consumer’s behaviour will not understand at these early stages, smart grid initiatives risk failing to realise their full potential. Observing of consumers in their social context has to start at an early stage of smart grid implementation and be included in the development process, to deliver the expected goals. The survey presented hereafter, considers the role of users in microgrids and the context, in which, such roles might emerge. Two contrasting visions of the smart grid are given. The centralized approach, based on current institutional arrangements and the decentralized approach, based on future distributed generation and microgrids. As defined before, the purpose is that smart grid designs must look beyond technology and recognize that energy consumers are those who are actively engaged with energy. Due to future smart grid involvement of consumers, two contrasting visions are identified. Energy consumers and energy citizens are distinguished by their orientation as energy end-users and energy system participations respectively. Energy consumer frame is a consequence of the same paradigm that drives power system, and they remain passive. Contrary, energy citizens align with the microgrids systems and distributed generation where they are active [2]. The data for the model used in this paper were derived from a survey that was carried out on Polytechnic of Međimurje County in Čakovec, on 402 participants. Thirteen questions covered the survey on the energy consumption habits, preferences and attitudes in today’s and future power system in the Liker-scale form with scores from 1 (strongly disagree) up to 5 (strongly agree). The sample composition by the age, income, education and employment structure indicates that a large proportion of the sample are students from various departments, from economics, computing to sustainable development [9]. The data were analysed using a logistic regression model. Binary logistic regression estimates the probability that a characteristic is present (in this case, that a consumer will be passive) given the values of explanatory variables [10]. Explanatory variables in the model are socio-economic characteristics of consumers and financial incentives. Simultaneously tensions between two visions, they are clearly not mutually exclusive and in practice, they are likely to co-exist. Conclusively, the most efficient smart grid will be one in which, despite smart devices, will be included smart users. As it has been tested, socio-economic factors don’t impact the behaviour of energy consumers. However, the economic initiatives impact energy consumer’s behaviour, so enabling smart grid market will positively influence and encourage them to become energy citizens. The further study explores current and potential energy behavioural adaptions in Croatia during the smart grid transition period. A focus-group methodology was made to a reprehensive sample of the particular segment of Croatian residential consumers, evaluating current energy behaviours and questionable future actions for three different scenarios of the smart grid. The reserach examines characteristics and the willingness of consumers, regarding the different level of engagement in the smart grid environment. The central research questions are: What is the current involvement of Croatian consumers in energy consumption? What are relevant factors for adopting further behavioural adoption in Croatia? What are the preferences towards the different levels of engagement in smart grid environment? The aim of the research is to make a useful contribution to help utilities, policy makers and other various stakeholders involved in smart grid transition process. The research of focus group has been carried out at the Polytechnic of Međimurje in Čakovec during April and May 2016. Four groups participated in the study, highly qualified consumers, categorised due to the status of involvement regarding involvement in education process at the Polytechnic. A group of full-time students (R) with eight participants, a group of part-time students (I) with seven participants, a group of teaching staff (N) with four members and administrative and support staff (A) with four participants, altogether 23 persons. As today’s students will be users of the smart grid, the emphasis is put on student population. However, another reason why the research was carried out within higher education is, as mentioned in [11], they will be the first community to adopt the smart grid technology. The research consisted of three sections. In the first part, participants filled in an entry form with basic socio-demographic characteristics, habits linked to energy consumption and the impact of the present economy on their usage of energy [9]. The second part included a short presentation on three different models of the future grid and ways of using energy in a household and its distribution. Model A represents uncontrolled consumption in a household consisting of home automated appliances enabling remote control, an electric vehicle and a small PV power plant. The house is supplied with electricity from a PV power plant or the grid. In the case of surplus electricity production, electricity is sent back into the grid, while in the event of lack of electricity, a shortage is compensated from the grid. The consumption of electricity for household appliances and charging the electric vehicle is not controlled. Model B represents controlled consumption without an energy storage system. The previous model is updated in the way that the consumption of household appliances is optimised due to the production of PV power plant. In the case of surplus or shortage, electricity is sent to or taken from the grid. The electric vehicle is uncontrolled load, charged when connected to the grid. Model C represents controlled consumption integrating an energy storage system, meaning that the electric vehicle, when connected to the house grid, can behave like a load or storage unit. The flow of energy from the battery will be determined by the household automation system, so that dependence on the grid will be decreased. In the case of a surplus of production, electricity will be stored in the battery of the electric vehicle or sent back to the grid. The consumer will personally set the settings, depending on the level of engagement in the electricity market. It can be concluded that, by using different approaches, the level of consumer engagement in the smart grid will be increased. Afterwards, a discussion with consumers was continued about their electricity usage, existing electric grid, topics about new technologies which will be integrated into the smart grid and preferences about the level of their engagement regarding different models and possibilities. The decrease of electricity consumption through measures of energy efficiency in future smart grid will not be enough due to the significant role of intermittent production capacity based on distributed energy resources. To satisfy consumers’ energy demands, they will be enabled to control their consumption with the aim of balancing production and consumption. Consumers will play a vital role in the realisation of the smart grid. Therefore, infrastructural and institutional support is necessary. It is important to understand that without a smart user, there will not be a smart grid [12]. Economics, behavioural and social psychology and technology diffusion focus on the individual as a decision maker, sociology questions the relevance of individually framed decision theories and highlights the social and technological construction of behaviour. Terms related to energy efficiency were familiar to participants. Hence, some positive impact in this field can be seen. From existing technologies used, the integration of the solar system is the most popular one, especially the solar water heating system. Participants revealed they are not informed about their energy consumption, especially new generations of students. Thus, this can be used as an advantage, since their concept of energy consumption can be modified, especially in further stages of their lives, when they will build their houses. The majority of the teaching and administration staff is using advantages of lower priced tariffs, especially for significant loads. The research affirmed that it would not be possible to control habits related to food preparing and cooking, and therefore, loads in a kitchen, except dishwashers, will be categorised as uncontrolled loads. The focus group methodology has confirmed that participants reacted positively on presented smart grid technologies. As an advantage of upgraded electric grid, the majority of participants would accept using the automation system to the level where they could control the settings independently from the central system. The smart home system, due to research, was familiar mostly to students and they were highly motivated to use this kind of system. The data privacy, storage, and user friendliness of the system are important categories of smart grid systems. The members expressed they expect to be educated in some way so that they would be familiar with all kinds of possibilities of smart grid systems. The research represented that just one of 23 households have changed its energy supplier due to the liberalisation phase of the energy market. However, in the case of possessing the distributed energy resource, participants showed their motivation to be engaged in the energy market. They think that individuals can set an example to grid users and motivate them to act sustainably. To sum up, this research has contributed to the current literature by exploring Croatian residential users, especially potential future smart grid users, identifying the most relevant factors and strategies for facilitating the behavioural change and detailing preferences towards smart grid technologies according to presented models. Although these results apply to a particular segment of Croatian population, they are not generalised to the overall population. Since the focus group research mainly assessed the willingness to engage in certain actions, future research should also include the evaluation of practical activities with real-world smart grid projects. Hereafter is presented testing model of 23 residential buildings with integrated renewable energy sources. For representative testing model it is selected self-sustainable house of the University of Zagreb, designed for international competition Solar Decathlon Europe 2014 [13]. The aim of “Concept Membrain” was to satisfy energy needs out of renewable energy sources, primarily Sun. The house was designed guiding the concept of “my first home”. The inspiration for the house was membrane as the vital components of all living organisms which are directly contacting with the environment, while substances selectively circulate in and out of the intracellular space. The concept intended to be a model for a future building as a self-sustainable prefabricated house, in region of Croatia. In this respect, photovoltaic cells were designed, to precisely 5kW, each, so study was based on those parameters. The algorithm modelled in research can be applied to the home energy management unit embed in the smart meters, which will probably work as a load control unit. If applied to a group of interconnected consumers, in the grid, it can be achieved coordinated management. As mentioned before, the key component of establishing demand side management is the smart meter. Smart meter is connected with all home appliances to measure electricity consumption and also to determine the total power requirements based on power consumptions of all appliances. On the other side, is also connected with user interface to collect user’s own power consumption plan so scheduling information can be displayed. Combining all input data, smart meter will optimize the hourly consumption and schedule all appliances [14]. Home appliances can be divided in three categories, depending on their power consumption pattern, excluding EVs. Non-shiftable appliances such as TV or fridge have a fixed operational period and power requirement, so they aren’t suitable for optimization. Time-shiftable appliances such as washing machines can be scheduled to suitable time, but their power pattern must be followed. Power-shiftable appliances such as water boilers have a set start time but their power pattern can be adjusted depending on optimization needs. EVs are declared as a separate category due to battery life time saving. It is necessary to enable effective home area communication network for the system [15]. When connected to the electric grid, EVs can be used as energy storage system. Household load can be managed by home demand side management to shift energy consumption due to energy production. Energy production is enabled with solar powered building, providing enough energy to fulfill minimum energy needs of household. Small microgrid can ensure and lower grid dependence, so in the paper it is represented how much energy needs to be provided form the electric grid. Thus, the mixed integer programming method is used for optimization of model consisted of household appliances in solar powered buildings and a sizable number of EVs minimizing. Results will indicate that the usage of conventional generation supplied from the electric grid is minimized. The mathematical model confirms that EVs can be used as energy storage system, buffering the solar energy and the consumption of household appliances can be easily postponed in order to enable future stabile and reliable microgrid systems. The algorithm is tested in GAMS software and can be used to test microgrid stability. The proposed optimization methods are modeled regarding the level of consumer engagement, categorization of household load and integration of electric vehicles as the possible storage system. The basic model A represents microgrid without the storage system with uncontrolled consumption. The model B represents microgrid without the storage system, with controlled household load and uncontrolled consumption of electric vehicles. The model C represented microgrid with the integrated storage system and controlled consumption of load and electric vehicles. The model D represents microgrid with controlled consumption of the load and electric vehicles, but without storage system. The optimization results in model A represent that during the morning and evening periods, microgrid needs support from the electric grid, but during the day, the significant amount of produced energy is sent to the electric grid. For model A 75 % of produced energy in the microgrid is sent back to the electric grid, for model B is 3 % lower, model C 2 % lower and for model D is 10 % lower. This represents that model D uses 15 % more produced energy in the microgrid. During the night periods, the electric vehicles are contributing to the electric grid even with 10 % of the energy produced in the microgrid. In model A, the dependence is almost 50 % of the installed power of the microgrid, in model B this is 32 %. I model C and D, dependence on the electric grid does not cross 9 % of installed power. Model C and D decreases the dependence on the grid, regarding model B for 3,7 times less, and for model A for 5,8 times less. |
