Thesis (MScEng)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: In the last century the earth has experienced an increase in the global mean temperature, with the main contributing factor being the increase in greenhouse gasses. Evidence indicates that the burning of fossil fuels, critical in the supply of energy, contributed towards three quarters of the carbon dioxide (CO2) increase. In 2008 South Africa reached electricity capacity constraints. A subsequent economic downturn experienced in the country, brought about by the worldwide economic recession, has relieved some of the strain on the electricity supply system. However, consumption levels are returning to those experienced during 2008 and no new base load power stations have been added. Short-term capacity constraints can be managed by shifting the peak demand, but the electricity shortage can only be avoided by adding additional capacity or reducing the overall electricity consumption. Supply-side solutions are both overdue and too expensive. The only solutions that can provide lasting results are demand-side solutions. During the past few years the Energy Efficiency and Demand-side Management (EEDSM) programme implemented by South Africa’s electricity supply utility, Eskom, has gained prominence. This programme relies heavily on calculating the savings incurred through any demand-side intervention. Energy audits enable the calculation of various consumption scenarios and can provide valuable insight into load operation and user behaviour. Energy audits involve a two-part procedure consisting of load surveying and an analysis. This thesis describes the development of both these procedures, combined into a single application. The application has been tested and provides an accurate and effective tool for simulating consumption and quantifying savings for various load adjustments. The results gained from the auditing application surpassed the expectations and provides the user with a sufficient base-line consumption estimate. The results do not reflect day-to-day variations, but the simulations are sufficient to quantify savings and determine whether demand-side interventions are financially viable. The application also presents a benchmark for the type of applications required to successfully implement an EEDSM programme. / AFRIKAANSE OPSOMMING: In die afgelope eeu het die aarde se gemiddelde temperatuur toegeneem, met die toename in kweekhuisgasse as die grootste bydraende faktor. Dit wil ook voorkom asof die verbranding van fossielbrandstowwe, wat noodsaaklik is vir die verskaffing van energie, verantwoordelik is vir driekwart van die toename in koolstofdioksied (CO2). Gedurende 2008 het Suid-Afrika elektrisiteitsbeperkings bereik. Die daaropvolgende ekonomiese afswaai wat in die land ervaar is weensdie wêreldwye ekonomiese resessie, het van die druk op die elekriese netwerk verlig. Verbruikersvlakke is egter besig om terug te keer na waar dit in 2008 was, maar geen nuwe basislas-kragstasies is gebou nie. Op die kort termyn kan die kapasiteitsbeperkings bestuur word deur die aanvraag te verskuif, maar die elektrisiteitstekort kan op die lang duur slegs vermy word deur bykomende kapasiteit by te voeg of die totale aanvraag te verminder. Toevoerkant-oplossings is beide agterstallig en te duur. Die enigste oplossings wat blywende resultate kan lewer, is dus aan die verbruikerkant. In die afgelope paar jaar het die effektiewe bestuur van energieverbruik baie aansien geniet. Die nasionale energievoorsiener, Eskom, het ook 'n program geloods om te help met die implimentering van energiebesparingmaatreëls. Die implementering van energie-oudits om met die kwantifisering van besparings te help, is van integrale belang vir die sukses van die program. Energie-oudits stel die eindverbruiker in staat om verskeie verbruiksmoontlikhede te beproef en sodoende waardevolle inligitng te verkry rakende die verbruikspatrone van die fasiliteit. Energie-oudits behels 'n tweeledige proses, bestaande uit 'n lasopname en 'n verbruiksanalise. Hierdie proefskrif beskryf die ontwikkeling van 'n stelsel wat beide die prosesse kombineer in 'n enkele applikasie. Die applikasie is getoets en bied 'n akkurate en doeltreffende instrument om verbruik te simuleer en besparings te kwantifiseer vir verskeie verbruiksmoontlikhede. v Die resultate van die oudit het die aanvanklike verwagtinge oortref en voorsien verbruikers van 'n goeie skatting van die basisverbruik van 'n fasiliteit. Die resultate weerspieël nie dagtot- dag variasies nie, maar die simulasies is voldoende om besparings te kwantifiseer en help om die finansiële lewensvatbaarheid van verbruikerskant-intervensies te bepaal. Die program bied ook 'n verwysingspunt vir applikasies wat besparingstudies wil implementeer.

Fuel supply issues are a major concern in remote island communities and this is an engineering field that needs to be analyzed in detail for transition to sustainable energy systems. Power generation in remote communities such as the islands of the Maldives relies on power generation systems primarily dependent on diesel generators. As a consequence, power generation is easily disrupted by factors such as the delay in transportation of diesel or rises in fuel price, which limits shipment quantity. People living in remote communities experience power outages often, but find them just as disruptive as people who are connected to national power grids. The use of renewable energy sources could help to improve this situation, however, such systems require huge initial investments. Remote power systems often operate with the help of financial support from profit-making private agencies and government funding. Therefore, investing in such hybrid systems is uncommon. Current electrical power generation systems operating in remote communities adopt an open loop control system, where the power supplier generates power according to customer demand. In the event of generation constraints, the supplier has no choice but to limit the power supplied and this often results in power cuts. Most smart grids that are being established in developed grids adopt a closed loop feedback control system. The smart grids integrated with demand side management tools enable the power supplier to keep customers informed about their daily energy consumption. Electric utility companies use different demand response techniques to achieve peak energy demand reduction by eliciting behavior change. Their feedback information is commonly based on factors such as cost of energy, environmental concerns (carbon dioxide intensity) and the risk of black-outs due to peak loads. However, there is no information available on the significant link between the constraints in resources and the feedback to the customers. In resource-constrained power grids such as those in remote areas, there is a critical relationship between customer demand and the availability of power generation resources. This thesis develops a feedback control strategy that can be adopted by the electrical power suppliers to manage a resource-constrained remote electric power grid such that the most essential load requirements of the customers are always met. The control design introduces a new concept of demand response called participatory demand response (PDR). PDR technique involves cooperative behavior of the entire community to achieve quality of life objectives. It proposes the idea that if customers understand the level of constraint faced by the supplier, they will voluntarily participate in managing their loads, rather than just responding to a rise in the cost of energy. Implementation of the PDR design in a mini-grid consists of four main steps. First, the end-use loads have to be characterized using energy audits, and then they have to be classified further into three different levels of essentiality. Second, the utility records have to be obtained and the hourly variation factors for the appliances have to be calculated. Third, the reference demand curves have to be generated. Finally, the operator control system has to be designed and applied to train the utility operators. A PDR case study was conducted in the Maldives, on the island of Fenfushi. The results show that a significant reduction in energy use was achieved by implementing the PDR design on the island. The overall results from five different constraint scenarios practiced on the island showed that during medium constrained situations, load reductions varied between 4.5kW (5.8%) and 7.7kW (11.3%). A reduction of as much as 10.7kW (15%) was achieved from the community during a severely constrained situation.

Using chilled water within South African gold mines is paramount to the purpose of extracting gold ore efficiently. Using water for cooling, drilling and sweeping and the release of underground fissure water causes the accumulation of vast amounts of water in underground dams. Deep mines use cascading pump systems for dewatering, which is an electrical energy intensive dewatering method. Due to the recent equalisation of demand to generation capacity of electrical energy in South Africa, various methods towards demand side reduction have been implemented. With the introduction of a time-of-use (TOU) tariff structure by Eskom, the implementation of projects that shift load from peak TOU times to times of the day when electrical energy is less expensive has increased. To enable load shifting on mine dewatering pumps, preparation before and recovery after peak TOU is needed for effective results. This induces a preparation- and comeback load in the standard TOU. With an annual increase in TOU tariffs and the rate of increase of standard TOU being greater than that of the peak TOU, a reduction in electrical energy consumption before and after peak TOU is needed. To enable this, a step-by-step control technique was developed to promote the shifting of load from standard- to off-peak TOU, while still realising a full load shift from peak TOU. This technique entails dynamic control ranges of underground dam levels as opposed to the conventional constant control range method. Two case studies were used to test the developed technique. Results indicated significant additional financial savings when compared to conventional control methods. Additional savings of R1,096,056.65 and R579,394.27 per annum were respectively achieved for both case studies. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2014

The world is currently experiencing major issues in the energy sector. The ever-growing human population, limited energy resources and the effect of greenhouse gas emissions have become major global concerns for the energy sector, including the electricity generation sector. This dilemma caused electricity providers to revise their generation methods and created a major need for consumers to utilise electricity more efficiently. Demand side management (DSM) is one initiative developed for consumers to efficiently utilise electricity. Due to their high electricity consumption and technical skills, mines are ideal targets for the implementation of DSM strategies. Therefore, the focus of this study was to investigate South African mines for possible implementation of DSM strategies on their compressed air networks. Compressed air networks at South African mines are relatively old and inadequately maintained. This causes inefficient distribution and use of compressed air. The study will therefore focus on reconfiguring mining compressed air networks for cost savings. Cost savings include financial savings on electricity bills, implementation costs and decreased maintenance. Through several investigations, the possibility of implementing energy savings strategies to reconfigure the compressed air networks of two South African mines was identified. Reconfiguring the networks would respectively entail interconnecting two shafts and relocating a compressor from an abandoned shaft to a fully productive shaft. Theoretical simulations were developed to determine the networks’ responses to the reconfiguration strategies. The simulations assisted in exposing the viability of implementing the reconfiguration strategies on the respective compressed air networks. Positive responses were obtained from the simulations and proposals were made to the respective mines for possible implementation. The proposed initiatives were implemented on the respective mines’ compressed air networks. After implementation of the interconnection strategy, a consecutive three-month performance assessment period commenced to prove the viability of the proposed savings. An average power saving of 1 700 kW was achieved during the performance assessment period. The proposed initiative to relocate the compressor is currently being implemented. A financial saving of approximately R8.9 million per annum was achieved by implementing the interconnection strategy. The large financial saving was due to the utilisation of the mine’s salvaged equipment. Further savings were achieved by the decreased maintenance on the mine’s compressors. Due to the successful implementation of the interconnection strategy, it is safe to state that cost savings can be achieved by reconfiguring mining compressed air networks. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2014

Assessing the impact of domestic low-carbon technologies on the electricity distribution network requires a detailed insight into the operation of networks and the power demands of consumers. When used on a wide-scale, low-carbon technologies, including domestic scale micro-generation, heat pumps, electric vehicles and flexible demand, will change the nature of domestic electricity use. In providing a basis for the quantification of the impact upon distribution networks, this thesis details the construction and use of a high-resolution integrated model that simulates both existing domestic electricity use and low voltage distribution networks. Electricity demand is modelled at the level of individual household appliances and is based upon surveyed occupant time-use data. This approach results in a simulation that exhibits realistic time-variant demand characteristics, in both individual dwellings, as well as, groups of dwellings together. Validation is performed against real domestic electricity use data, measured for this purpose, from dwellings in Loughborough in the East Midlands, UK. The low voltage distribution network is modelled using real network data, and the output of its simulation is validated against measured network voltages and power demands. The integrated model provides a highly detailed insight into the operation of networks at a one-minute resolution. This integrated model is the main output of this research, alongside published articles and a freely downloadable software implementation of the demand model.

Various forecasting tools, based on historical data, exist for planners of national networks that are very effective in planning national interventions to ensure energy security, and meet carbon obligations over the long term. However, at a local community level, where energy demand patterns may significantly differ from the national picture, planners would be unable to justify local and more appropriate intervention due to the lack of appropriate planning tools. In this research, a new methodology is presented that initially creates a virtual community of households in a small community based on a survey of a similar community, and then predicts the energy behaviour of each household, and hence of the community. It is based on a combination of the statistical data, and a questionnaire survey. The methodology therefore enables realistic predictions and can help local planners decide on measures such as embedding renewable energy and demand management. Using the methodology developed, a study has been carried out in order to understand the patterns of electricity consumption within UK households. The methodology developed in this study has been used to investigate the incentives currently available to consumers to see if it would be possible to shift some of the load from peak hours. Furthermore, the possibility of using renewable energy (RE) at community level is also studied and the results presented. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. A new pricing criteria has therefore been developed to help developers and planners of local communities to understand the cost of intervention. Conclusions have been drawn from the work. Finally, suggestions for future work have been presented.

It is more than a fact that electrical energy is a main production factor of every economic activity. Since electrical power is not easy to store, it needs to be consumed as it is generated in order to keep a constant balance between supply and demand. As a result, for developing an efficient energy market it is significant to create a method for accurately forecasting the electricity consumption. This thesis describes a method for analyzing data provided by the ENTSO-E transparency platform. The ENTSO-E (European Network of Transmission System Operators) is a network of electricity operators from 36 countries across Europe. Its main objective is to provide transparency concerning data of electricity generation and consumption in Europe in order to promote the development of efficient and competitive electricity markets. By using the method described in this thesis, one may use historical data provided by ENTSO-E to forecast the electricity consumption of an EU country for the years to come. As an example, data of electricity consumption in Greece during the years 2015-2018 have been used in order to calculate the average load demand of a weekday during the year 2030. On the other hand, in order to correctly predict the electricity demand of a specific region over the next decade, one should take into account some crucial parameters that may influence not only the evolution of the load demand, but also the fuel mix that will be used in order to cover our future electricity needs. Advances in power generation technologies, evolution of fuel prices, expansion of electricity grid and economic growth are a subset of parameters that should be taken into account for an accurate forecast of the electricity consumption in the long run. Particularly for Greece, a set of parameters that may affect the electricity consumption are being computationally analyzed in order to evaluate their contribution to the load demand curve by the year 2030. These include the interconnection of Greek islands to the mainland, the development of Hellinikon Project and the increase of the share of electric vehicles. The author of this thesis has developed code in Python programming language that can be found in the Appendix. These scripts and functions that implement most of the calculations described in the following chapters can also be used for forecasting the load demand of other EU countries that are included in the ENTSO-E catalogue. The datasets used as input to these algorithms may also be used from the readers to identify more patterns for predicting the load demand for a specific region and time. A sustainable energy system is based on consumers with environmental awareness. As a result, citizens living inside the European Union should become a member of a community that promotes energy saving measures, investments in renewable energy sources and smart metering applications.

Neste trabalho foi desenvolvida uma metodologia para realizar a gestão ativa da demanda de energia elétrica de consumidores, providos de armazenamento de energia elétrica e geração solar fotovoltaica, inseridos em redes inteligentes. Tal metodologia pode ser utilizada em instalações residenciais e comerciais. Utilizando estratégias de otimização e inteligência artificial, a metodologia configura um sistema de tomada de decisão para o gerenciador do conversor da bateria, que realiza a gestão da energia armazenada, visando reduzir o custo com energia elétrica para o consumidor final. Esta gestão propicia contribuição com a distribuidora em forma de aumento da reserva de capacidade da rede elétrica nos casos em que a tarifa de energia elétrica for mais cara no horário de pico. De qualquer forma, há potencial postergação da necessidade de expansão da rede elétrica e redução de impactos ambientais advindos da geração convencional de energia elétrica, uma vez que tal gestão de energia propicia redução de consumo de energia elétrica da rede. O mesmo sistema de tomada de decisão do gerenciador do conversor da bateria pode ser utilizado em vários consumidores com características semelhantes (mesmo tipo, localização e tarifação de energia elétrica, e perfil de consumo similar), uma vez que tal sistema é composto por uma rede neural treinada com dados locais. Estudo de caso foi conduzido considerando consumidor residencial na cidade de São Paulo. Foram construídos quinze perfis de consumo, que foram combinados com três perfis de geração solar. A metodologia apresentou desempenho satisfatório, tanto na avaliação da etapa de otimização quanto de treinamento da rede neural, uma vez que as curvas de armazenamento de energia apresentaram comportamentos próximos aos esperados. O sistema de tomada de decisão também respondeu de forma adequada, alterando a curva de carga do consumidor vista pela rede de forma a reduzir o custo diário com energia elétrica e o consumo de energia no horário de pico da residência em todos os casos estudados. A análise econômica apontou a necessidade de encontrar formas de tornar a iniciativa positiva do ponto de vista econômico no estudo de caso realizado. / This work presents a methodology developed to perform the active demand side management for consumers, provided with energy storage and solar photovoltaic power, inserted in smart grids. Such methodology can be used in residential and commercial installations. Using optimization and artificial intelligence strategies, the methodology sets up a decision-making system for the battery converter manager, which performs energy storage management, in order to reduce the cost with electricity for the final consumer. This management contributes with the utility increasing the grid reserve capacity when the electricity tariff is more expensive during peak hours. Anyway, there is potential postponement of the need to expand the grid, and environmental impacts reduction from conventional power generation, since such power management provides a reduction of the grid electricity consumption. The same decision-making system of the battery converter manager can be used in several consumers with similar characteristics (same type, location and electricity tariff, and similar consumption profile), since this system is composed by a neural network trained with local data. A case study was conducted considering household in the city of São Paulo. Fifteen consumption profiles were built, which were combined with three solar generation profiles. The methodology presented satisfactory performance both in the evaluation of the optimization stage and the neural network training stage, since the energy storage curves presented behaviors close to those expected. The decision-making system also responded adequately, changing the consumer load curve seen by the grid in order to reduce the daily electricity cost, and energy consumption at peak hours of the household in all cases studied. The economic analysis pointed to the need to find ways to make the initiative positive from an economic point of view in the case study carried out.

In accordance with the main aim of the study, a widely accessible, modifiable tool was created for parties interested in maintaining the national electricity supply network and parties interested in informing policy on plug-in vehicle adoption schemes and recharging behaviour control. The Parry Tool enables the user to incorporate present limits to plug-in vehicle recharging demand scheduling as imposed by the state of present technology (no third party mechanism for monitoring and control of recharging), present human travel behaviour needs and existing patterns in electricity usage; into the investigation of the impacts of recharging demand impacts and the design of mitigation measures for deflecting (parrying) worst case scenarios. The second aim of the project was to demonstrate the application of the Parry Tool. The multidisciplinary/interdisciplinary information gathered by the Parry Tool was used to produce national demand profiles for plug-in vehicle recharging demand, calculated using socioeconomic and travel behaviour-estimated population sizes for plug-in eligible vehicles and vehicle usage patterns, which were added to existing national electricity demand for a chosen test week – this was the first scenario subsequently tested. The information gathered by the Parry Tool was then used to inform the design of two demand management methods for plug-in vehicle recharging: Recharging Regimes and weekly recharging load-shifting – these were the second and third scenarios subsequently tested. Unmitigated simultaneous recharging demand in scenario 1 (all vehicles assumed to recharge at home upon arrival home every day) severely exacerbated peak demand, raising it by 20% above the highest peak in existing demand for the year 2009 over half an hour from 58,554 MW to 70,012 MW – a challenge to the generation sector. This increased the difference between daily demand minima and maxima and made the new total demand have sharper peaks – a challenge for grid regulators. Recharging Regimes in scenario 2 split the estimated national plug-in vehicle populations into groups of different sizes that started recharging at different times of the day, with the word ‘regime’ being applied because the spread of start times changed over the course of the test week from workdays to weekend. This avoided exacerbation of the peak and reduced the difference between daily demand minima and maxima by raising minima, providing a load-levelling service. Scenario 3 embellished the Recharging Regimes with workday-to-weekend recharging load-shifting that therefore took better advantage of the often overlooked weekly pattern in existing demand (demand being higher on workdays than weekends), by allowing partial recharging of a segment of the plug-in vehicle population. Limited consideration of the impact of changing vehicle energy usage (for which distance travelled was assumed to proxy in this study) showed that the more vehicles used their batteries during the day, the better the levelling effect offered by Recharging Regimes. Greater utilisation of battery capacity each day, however, can also be assumed to lessen the potential for workday-to-weekend load levelling, because load-shifting depends upon vehicles being able to partially recharge or defer recharging to later days and still meet their travel needs plus keep a reserve State Of Charge (SOC) for emergency and other unplanned travel. Whilst altering vehicle energy usage did not change the finding that unmitigated simultaneous recharging exacerbated existing peak demand, it was noted that when limited mileage variation was considered this sharpened the profile of total demand – the rise and fall of the new peak far steeper than that of the original peak in existing demand. The Parry Tool combines a series of integrated methods, several of which are new contributions to the field that use UK data archives but may potentially be adapted by researchers looking at energy issues in other nations. It presents a novel fossil-fuel based justification for targeting road transport – acknowledging energy use of fossil fuel as the originator of many global and local problems, the importance of non-energy use of petroleum products and subsequent conflicts of interest for use, and a fossil fuel dependency based well-to-wheel assessment for UK road transport for the two energy pathways: electricity and petroleum products. It presents a method for the recalculation and ranking of top energy use/users using national energy use statistics that better highlights the importance of the electricity industry. It also presents the first publicly documented method for the direct consultation and extraction of vehicle-focused statistics from the people-focused National Travel Survey database, including a travel behaviour and household income-based assessment of plug-in vehicle eligibility, used to scale up to national estimates for battery electric and plug-in electric hybrid vehicle (BEV and PHEV) national population sizes. The work presented here is meant to allow the reader to perceive the potential benefits of using several resources in combination. It details the Parry Tool, a framework for doing so, and where necessary provides methods for data analysis to suit. It should however be noted that methods were kept as simple as possible so as to be easily followed by non-specialists and researchers entering the field from other disciplines. Methods are also predominantly data-exploratory in nature: strong conclusions therefore should not be drawn. Rather, the work here should be seen as a guideline for future work that may more rigorously study these combined topics and the impacts they may have upon plug-in vehicle ownership, usage behaviour, impacts of recharging upon the national network and the design of mitigation measures to cope with this new demand.

Neste trabalho foi desenvolvida uma metodologia para realizar a gestão ativa da demanda de energia elétrica de consumidores, providos de armazenamento de energia elétrica e geração solar fotovoltaica, inseridos em redes inteligentes. Tal metodologia pode ser utilizada em instalações residenciais e comerciais. Utilizando estratégias de otimização e inteligência artificial, a metodologia configura um sistema de tomada de decisão para o gerenciador do conversor da bateria, que realiza a gestão da energia armazenada, visando reduzir o custo com energia elétrica para o consumidor final. Esta gestão propicia contribuição com a distribuidora em forma de aumento da reserva de capacidade da rede elétrica nos casos em que a tarifa de energia elétrica for mais cara no horário de pico. De qualquer forma, há potencial postergação da necessidade de expansão da rede elétrica e redução de impactos ambientais advindos da geração convencional de energia elétrica, uma vez que tal gestão de energia propicia redução de consumo de energia elétrica da rede. O mesmo sistema de tomada de decisão do gerenciador do conversor da bateria pode ser utilizado em vários consumidores com características semelhantes (mesmo tipo, localização e tarifação de energia elétrica, e perfil de consumo similar), uma vez que tal sistema é composto por uma rede neural treinada com dados locais. Estudo de caso foi conduzido considerando consumidor residencial na cidade de São Paulo. Foram construídos quinze perfis de consumo, que foram combinados com três perfis de geração solar. A metodologia apresentou desempenho satisfatório, tanto na avaliação da etapa de otimização quanto de treinamento da rede neural, uma vez que as curvas de armazenamento de energia apresentaram comportamentos próximos aos esperados. O sistema de tomada de decisão também respondeu de forma adequada, alterando a curva de carga do consumidor vista pela rede de forma a reduzir o custo diário com energia elétrica e o consumo de energia no horário de pico da residência em todos os casos estudados. A análise econômica apontou a necessidade de encontrar formas de tornar a iniciativa positiva do ponto de vista econômico no estudo de caso realizado. / This work presents a methodology developed to perform the active demand side management for consumers, provided with energy storage and solar photovoltaic power, inserted in smart grids. Such methodology can be used in residential and commercial installations. Using optimization and artificial intelligence strategies, the methodology sets up a decision-making system for the battery converter manager, which performs energy storage management, in order to reduce the cost with electricity for the final consumer. This management contributes with the utility increasing the grid reserve capacity when the electricity tariff is more expensive during peak hours. Anyway, there is potential postponement of the need to expand the grid, and environmental impacts reduction from conventional power generation, since such power management provides a reduction of the grid electricity consumption. The same decision-making system of the battery converter manager can be used in several consumers with similar characteristics (same type, location and electricity tariff, and similar consumption profile), since this system is composed by a neural network trained with local data. A case study was conducted considering household in the city of São Paulo. Fifteen consumption profiles were built, which were combined with three solar generation profiles. The methodology presented satisfactory performance both in the evaluation of the optimization stage and the neural network training stage, since the energy storage curves presented behaviors close to those expected. The decision-making system also responded adequately, changing the consumer load curve seen by the grid in order to reduce the daily electricity cost, and energy consumption at peak hours of the household in all cases studied. The economic analysis pointed to the need to find ways to make the initiative positive from an economic point of view in the case study carried out.