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Integrating data analytics, optimisation and dynamic control to support energy services has seen significant interest in recent years. Larger appliances used in an industry context are now provided with Internet of Things (IoT)-based interfaces that can be remotely monitored, with some also provided with actuation interfaces. The combined use of IoT and edge computing enables connectivity between energy systems and infrastructure, providing the means to implement both energy efficiency/optimisation and cost reduction strategies. We investigate the economic implications of harnessing IoT and edge/cloud technologies to support energy management for HVAC (Heating, Ventilation and Air Conditioning) systems in buildings. In particular, we evaluate the cost savings for building operations through energy optimisation. We use a real use case for energy optimisation as identified in the EU “Sporte2” project (focusing on the energy optimisation of sports facilities) and explore several scenarios in relation to costs and energy optimisation.

Steam accumulation is one of the most effective ways of thermal energy storage (TES) for the solar thermal energy (STE) industry. However, the steam accumulator concept is penalized by a bad relationship between the volume and the energy stored, moreover, its discharge process shows a decline in pressure, failing to reach nominal conditions in the turbine. From the economic point of view, between 60% and 70% of the cost of a steam accumulator TES is that of the pressure vessel tanks (defined as US$/kWhth). Since the current trend is based on increasing hours of storage in order to improve dispatchability levels in solar plants, the possibility of cost reduction is directly related to the cost of the material of pressure vessels, which is a market price. Therefore, in the present paper, a new design for steam accumulation is presented, focusing on innovative materials developed specifically for this purpose: two special concretes that compose the accumulation tank wall. Study of dosages, selection of materials and, finally, scale up on-field tests for their proper integration, fabrication and construction in prototype are the pillars of this new steam accumulation tank. Establishing clear and precise requirements and instructions for successful tank construction is necessary due to the highly sensitive and variable nature of those new concrete formulations.

The design of an energy management strategy is critical to improving the fuel efficiency of a vehicle system with an alternative powertrain system, such as hybrid electric vehicles or fuel cell electric vehicles. In particular, in fuel cell electric vehicles, the energy management strategy should consider system degradation and fuel savings because the hardware cost of the fuel cell system is much higher than that of a conventional powertrain system. In this paper, an easily implantable near-optimal energy management controller is proposed. The proposed controller distributes power generation between the fuel cell and the battery to simultaneously minimize system degradation and fuel usage. The controller is designed to consider the degradation cost and fuel cost in the framework of the equivalent consumption minimization strategy concept. The proposed controller was validated with a fuel cell electric vehicle model in MATLAB/Simulink (MathWorks, Natick, MA, USA). The proposed control strategy showed significant overall cost reduction compared to a thermostat control strategy and a conventional Equivalent Consumption Minimization Strategy (ECMS) strategy.

Due to globalization and increased market competition, forwarding companies must focus on the optimization of their international transport activities and on cost reduction. The minimization of the amount and cost of fuel results in increased competition and profitability of the companies as well as the reduction of environmental damage. Nowadays, these aspects are particularly important. This research aims to develop a new optimization method for road freight transport costs in order to reduce the fuel costs and determine optimal fueling stations and to calculate the optimal quantity of fuel to refill. The mathematical method developed in this research has two phases. In the first phase the optimal, most cost-effective fuel station is determined based on the potential fuel stations. The specific fuel prices differ per fuel station, and the stations are located at different distances from the main transport way. The method developed in this study supports drivers’ decision-making regarding whether to refuel at a farther but cheaper fuel station or at a nearer but more expensive fuel station based on the more economical choice. Thereafter, it is necessary to determine the optimal fuel volume, i.e., the exact volume required including a safe amount to cover stochastic incidents (e.g., road closures). This aspect of the optimization method supports drivers’ optimal decision-making regarding optimal fuel stations and how much fuel to obtain in order to reduce the fuel cost. Therefore, the application of this new method instead of the recently applied ad-hoc individual decision-making of the drivers results in significant fuel cost savings. A case study confirmed the efficiency of the proposed method.

This study examined the feasibility of green hydrogen as a transport fuel for the very heavy vehicle (VHV) fleet in New Zealand. Green hydrogen is assumed to be produced through water electrolysis using purely renewable energy (RE) as an electricity source. This study chose very heavy vehicles as a potential market for green hydrogen, because it is considered “low-hanging fruit” for hydrogen fuel in a sector where battery electrification is less feasible. The study assumed a large-scale, decentralized, embedded (dedicated) grid-connected hydrogen system of production using polymer electrolytic membrane (PEM) electrolysers. The analysis comprised three steps. First, the hydrogen demand was calculated. Second, the additional RE requirement was determined and compared with consented, but unbuilt, capacity. Finally, the hydrogen production cost was calculated using the concept of levelized cost. Sensitivity analysis and cost reduction scenarios were also undertaken. The results indicate an overall green hydrogen demand for VHVs of 71 million kg, or 8.5 PJ, per year, compared to the 14.7 PJ of diesel fuel demand for the same VHV travelled kilometres. The results also indicate that the estimated 9824 GWh of RE electricity that could be generated from consented, yet unbuilt, RE projects is greater than the electricity demand for green hydrogen production, which was calculated to be 4492 GWh. The calculated levelized hydrogen cost is NZD 6.83/kg. Electricity cost was found to be the most significant cost parameter for green hydrogen production. A combined cost reduction for CAPEX and electricity translates to a hydrogen cost reduction in 10 to 20 years.

Blockchain technology is currently one of the trends considered to have a tremendous future ahead. It ensures data security, data sharing protection and automation development—elements that are of colossal importance in the era of cloud solutions, big data and Internet of Things (IoT) reality. Additionally, blockchain technology allows one to create new programmable ecosystems on an unprecedented scale. The implementation of blockchain technology leads not only to improving the flow of documents and data storage, as is the case with the creation of shared service centers (SSCs), but—as this paper shows—allows one to reduce the carbon footprint when servicing SSCs at a considerably higher organizational level at the same time. The example of an SSC in Elbląg, Poland, proves that cloud solutions enabling electronic documents flow and data storage combined with blockchain technology are tools essential for further SSCs development. Furthermore, such tools allow us not only to obtain economic effects (i.e., cost reduction), but also to achieve positive ecological effects (i.e., carbon footprint reduction).

Small modular reactors (SMRs) represent a key area of interest to nuclear industry developers, which have been making significant progress during the past few years. Generally, these reactors are promising owing to their improved safety due to passive systems, enhanced containment efficiency, and fewer capital costs in comparison to traditional nuclear reactors. An important advantage of SMRs is their adaptability in being coupled to other energy-consuming systems, such as desalination plants (DPs) to create a cogeneration plant. Considering the serious challenges regarding the freshwater shortage in many regions of the world and the necessity of using low-carbon energy sources, it is advantageous to use SMR for supplying the required heat and electricity of DPs. As a high-performance desalination technology, the hybrid desalination (HD) systems can be exploited, which retain the advantages of both thermal and membrane desalination methods. In this study, several SMR coupling schemes to HD plants have been suggested. In performing a thermodynamic analysis of integrated SMR-DP, the International Atomic Energy Agency (IAEA) Desalination Thermodynamic Optimization Program (DE-TOP) has been utilized. It has been found that the use of relatively hot water from the SMR condenser leads to about 6.5 to 7.5% of total desalination cost reduction, where the produced electricity and hot steam extracted from low-pressure turbine were used to drive the HD system.

Boil-off gas (BOG) disposal in liquefied natural gas (LNG) tankers has long been considered inevitable owing to the constant vaporization of the LNG in the storage tanks, but results in energy waste and environmental pollution. To address these challenges, we developed a re-liquification system that can condense the BOG and return it to the storage tank. The re-liquification system was modeled, and a case study was conducted to evaluate the viability of the system. The energy waste, which was quantified by tonnes of oil equivalent (TOE), greenhouse-gas emissions in tonnes of carbon dioxide (TCO2), and cost reduction in millions of U.S. dollars (MUSD), was evaluated for five different tanker cruising speeds. The re-liquification system significantly reduced the average TOE, TCO2, and cost by up to 9120.40 TOE/year, 19,474.33 TCO2/year, and 1.9765 MUSD/year, respectively, for five different tanker speeds with multi-stage compression.

Climate change, other environmental impacts due to increased energy use worldwide, and the exhaustion of energy resources are some of the major challenges facing today’s society. Considering this, this paper assesses the importance of biomass-based heating and hot-water systems in the achievement of more sustainable buildings. Using a simplified calculation method, we jointly analyzed the potential operational cost savings and reduction of CO2 emissions that would be achieved when the traditional energy model, based on the use of fossil fuels, is replaced by biomass-based heating systems. Evidence stems from a case study in public buildings in the province of Pontevedra, in the northwest of Spain. The results of this research not only show a huge impact on CO2 emission reduction just by adapting the kind of fuel use, but also considerable annual cost reduction without compromising activity development and workers’ comfort. Thus, the findings obtained should encourage governments to support the transition toward cleaner sources of energy, acting as first movers toward a locally produced and renewable-based energy supply.

Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the aggregator side aiming at cost minimization. The framework consists of three architectural layers, namely, the consumer, aggregator and decision support system (DSS), forming a tri-layer optimization framework with multiple interacting objects, such as objective functions, variables, constants and constraints. The DSS layer is responsible for decision support by forecasting the day-ahead energy management requirements. The main purpose of this study is to achieve optimal management of energy resources, considering both aggregator and consumer preferences and goals, whilst abiding with real-world system constraints. This is conducted through detailed simulations using real data from a pilot, that is part of Terni Distribution System portfolio.

All over the world the reduction of greenhouse gas (GHG) emissions, especially in the transportation sector, becomes more and more important. Electric vehicles will be one of the key factors to mitigate GHG emissions due to their higher efficiency in contrast to internal combustion engine vehicles. On the other hand, uncoordinated charging will put more strain on electrical distribution grids and possible congestions in the grid become more likely. In this paper, we analyze the impact of uncoordinated charging, as well as optimization-based coordination strategies on the voltage stability and phase unbalances of a representative European semi-urban low voltage grid. Therefore, we model the low voltage grid as a three-phase system and take realistic arrival and departure times of the electric vehicle fleet into account. Subsequently, we compare different coordinated charging strategies with regard to their optimization objectives, e.g., cost reduction or GHG emissions reduction. Results show that possible congestion problems can be solved by coordinated charging. Additionally, depending on the objective, the costs can be reduced by more than 50% and the GHG emissions by around 40%.

The need for efficient electrical energy consumption has greatly expanded in the process industries. In this paper, efforts are made to recognize the electrical energy consumption in a two-echelon supply chain model with a stochastic lead-time demand and imperfect production, while considering the distribution free approach. The initial investments are made for quality improvement and setup cost reduction, which ultimately reduce electrical energy consumption. The inspection costs are considered in order to ensure the good qualities of the product. Centralized and decentralized strategies are used to analyze the proposed supply chain model. The main objective of this study is to reduce the overall cost through efficient electrical energy consumption in supply chain management by optimizing the lot size, the number of shipments, the setup cost, and the failure rate. A quantity-based transportation discount policy is applied to reduce the expected annual costs, and a service-level constraint is incorporated for the buyer to avoid a stockout situation. The impact of the decision variables on the expected total costs is analyzed, and sensitivity analysis is carried out. The results show a significant reduction in overall cost, with quality improvement and setup cost reduction ultimately reducing electrical energy consumption.

Driven by falling photovoltaic (PV) installation costs and potential support policies, rooftop PV is expected to expand rapidly in Thailand. As a result, the relevant stakeholders, especially utilities, have concerns about the net economic impacts of high PV adoption. Using a cost&ndash, benefit analysis, this study quantifies the net economic impacts of rooftop PV systems on three utilities and on ratepayers in Thailand by applying nine different PV adoption scenarios with various buyback rates and annual percentages of PV cost reduction. Under Thailand&rsquo, s current electricity tariff structure, Thai utilities are well-protected and able to pass all costs due to PV onto the ratepayers in terms of changes in retail rates. We find that when PV adoption is low, the net economic impacts on both the utilities and retail rates are small and the impacts on each utility depend on its specific characteristics. On the other hand, when PV adoption ranges from 9&ndash, 14% in energy basis, five-year retail rate impacts become noticeable and are between 6% and 11% as compared to the projected retail rates in 2036 depending on the PV adoption level. Thus, it is necessary for Thailand to make tradeoffs among the stakeholders and maximize the benefits of rooftop PV adoption.

This article develops a sustainable electricity supply chain mathematical model that assumes linear price-dependent customer demands where the price is a decision variable under setup cost and carbon emission. The sustainable electrical supply chain system contained: (a) power generation, (b) transmission substations, (c) distribution substations, and (d) customer. The production rates depend on the demand rate, and demand for electricity by the customers is dependent on the price of electricity where the electrical energy was generated and transmitted through multiple substations to customers. Moreover, we considered that the capacities of transmission rates, power generation, and distances in between two stations are associated with the distribution costs and transmission cost. Here, we used the theory of inventory to develop a new model and suggested a procedure to deduce an optimal solution for this model. Finally, a numerical example and sensitivity analysis are employed to illustrate the present study and with managerial insights.

Plummeting reserves and increasing demand of freshwater resources have culminated into a global water crisis. Desalination is a potential solution to mitigate the freshwater shortage. However, the process of desalination is expensive and energy-intensive. Due to the water-energy-climate nexus, there is an urgent need to provide sustainable low-cost electrical power for desalination that has the lowest impact on climate and related ecosystem challenges. For a large-scale reverse osmosis desalination plant, we have proposed the design and analysis of a photovoltaics and battery-based stand-alone direct current power network. The design methodology focusses on appropriate sizing, optimum tilt and temperature compensation techniques based on 10 years of irradiation data for the Carlsbad Desalination Plant in California, USA. A decision-tree approach is employed for ensuring hourly load-generation balance. The power flow analysis evaluates self-sufficient generation even during cloud cover contingencies. The primary goal of the proposed system is to maximize the utilization of generated photovoltaic power and battery energy storage with minimal conversions and transmission losses. The direct current based topology includes high-voltage transmission, on-the-spot local inversion, situational awareness and cyber security features. Lastly, economic feasibility of the proposed system is carried out for a plant lifetime of 30 years. The variable effect of utility-scale battery storage costs for 16–18 h of operation is studied. Our results show that the proposed design will provide low electricity costs ranging from 3.79 to 6.43 ¢/kWh depending on the debt rate. Without employing the concept of baseload electric power, photovoltaics and battery-based direct current power networks for large-scale desalination plants can achieve tremendous energy savings and cost reduction with negligible carbon footprint, thereby providing affordable water for all.

Developments, trends, business climate, conditions, factors influencing the efficiency and results of mergers and acquisitions (M&A) in the energy sector are explored in this research. PESTLE (political, economic, social, technological, legal, environmental) analysis was performed in order to determine the driving forces of M&As in the energy industry. Considering the motivation and main questions of the study, a sample of global M&A deals that have occurred during the period 1995–2020 has been analyzed. DataStream 5.1 database by Thomson Reuters was employed to identify the sample of global energy companies that took over another company in the period 1995–2020. According to the research, while the role and presence of M&As in the energy industry are increasing, the purpose of the M&A deals has changed remarkably. During 1995–2010, most M&A events were conducted in order to explore synergies and benefit from cost reduction. Since the last decade, firms are pursuing M&As in the search of growth opportunities, ensuring supply and reflecting demand for green development of ecological environment and ongoing changes in the nature of energy.

A growing interest in renewable energy resources has been observed for several years, due to their pollution-free nature, availability all over the world, and continuity. These facts make these energy resources attractive for many applications. In this work, the hybrid combination of a photovoltaic-biomass system is investigated as an energy source. This paper determines optimal sizing and cost reduction of grid-integrated renewable energy resources by using an intelligence optimization technique, the dragonfly algorithm. The efficiency of the proposed methodology is also compared with an existing technique, which uses the artificial bee colony (ABC) algorithm. The scope of this work is to reduce the annual total cost of power with a reduced number of solar panels. The monthly average solar radiation is used to compute the obtained power. The outcome of the proposed technique proves that the grid-connected system with an optimal number of components satisfactorily meets the needs of the village at a reduced price. The simulation results are carried out under the MATLAB environment. The comparison of results clearly demonstrates that the proposed system is much more efficient than the existing one.

Energy Storage Systems (ESSs) have recently been highlighted because of their many benefits such as load-shifting, frequency regulation, price arbitrage, renewables, and so on. Among those benefits, we aim at evaluating their economic value in frequency regulation application. However, unlike previous literature focusing on profits obtained from participating in the ancillary service market, our approach concentrates on the cost reduction from the perspective of a utility firm that has an obligation to pay energy fees to a power exchange. More specifically, we focus on the payments between the power exchange market and the utility firm as a major source of economic benefits. The evaluation is done by cost- benefit analysis (CBA) with a dataset of the Korean market while considering operational constraint costs as well as scheduled energy payments, and a simulation algorithm for the evaluation is provided. Our results show the potential for huge profits to be made by cost reduction. We believe that this research can provide a guideline for a utility firm considering investing in ESSs for frequency regulation application as a source of cost reduction.

A novel, fully decentralized strategy to coordinate charge operation of electric vehicles is proposed in this paper. Based on stochastic switching control of on-board chargers, this strategy ensures high-efficiency charging, reduces load variations to the grid during charging periods, achieves charge completion with high probability, and accomplishes approximate “valley-filling”. Further improvements on the core strategy, including individualized power management, adaptive strategies, and battery support systems, are introduced to further reduce power fluctuation variances and to guarantee charge completion. Stochastic analysis is performed to establish the main properties of the strategies and to quantitatively show the performance improvements. Compared with the existing decentralized charging strategies, the strategies proposed in this paper can be implemented without any information exchange between grid operators and electric vehicles (EVs), resulting in a communications cost reduction. Additionally, it is shown that by using stochastic charging rules, a grid-supporting battery system with a very small energy capacity can achieve substantial reduction of EV load fluctuations with high confidence. An extensive set of simulations and case studies with real-world data are used to demonstrate the benefits of the proposed strategies.

The life-cycle cost reduction of medium-class gas turbine power plants was investigated using the mathematical optimization technique. Three different types of gas turbine power cycles—a simple cycle, a regenerative cycle, and a combined cycle—were examined, and their optimal design conditions were determined using the sequential quadratic programming (SQP) technique. As a modeling reference, the Siemens SGT-700 gas turbine was chosen and its technical data were used for system simulation and validation. Through optimization using the SQP method, the overall costs of the simple cycle, regenerative cycle, and combined cycle were reduced by 7.4%, 12.0%, and 3.9%, respectively, compared to the cost of the base cases. To examine the effect of economic parameters on the optimal design condition and cost, different values of fuel costs, interest rates, and discount rates were applied to the cost calculation, and the optimization results were analyzed and compared. The values were chosen to reflect different countries’ economic situations: South Korea, China, India, and Indonesia. For South Korea and China, the optimal design condition is proposed near the upper bound of the variation range, implying that the efficiency improvement plays an important role in cost reduction. For India and Indonesia, the optimal condition is proposed in the middle of the variation ranges. Even for India and Indonesia, the fuel cost has the largest contribution to the total cost, accounting for more than 60%.

Trading on the energy market is a possible way to reduce the electricity costs of charging electric vehicles at public charging stations. In many European countries, it is possible to trade electricity until shortly before the period of delivery on so called intraday electricity markets. In the present work, the potential for reducing the electricity costs by trading on the intraday market is investigated using the example of the German market. Based on simulations, the authors reveal that by optimizing the charging schedule together with the trading on the intraday electricity market, the costs can be reduced by around 8% compared to purchasing all the required energy from the energy supplier. By allowing the charging station operator to resell the energy to the intraday electricity market, an additional cost reduction of around 1% can be achieved. Besides the potential cost savings, the impacts of the trading unit and of the lead time of the intraday electricity market on the costs are investigated. The authors reveal that the achievable electricity costs can be strongly affected by the lead time, while the trading unit has only a minor effect on the costs.

Advanced metering infrastructure (AMI) refers to the electricity service infrastructure between electricity consumers and suppliers and is technically essential for the realization of a smart grid environment. To implement AMI, various communications technologies are being used based on the application environment according to the utility. However, using a single communications method can give rise to attenuation in the downtown underground distribution line section or cause higher supply costs due to decreased density in the range from farming to fishing areas. A hybrid AMI is one solution to this problem. According to an economic analysis of previous AMI deployment, the cost to install a communications network accounts on average for 45% of the total cost. Since the installation cost of a communications network is influenced by the density of the installation environment, a hybrid AMI, which allows the configuration of a flexible network using both wired and wireless communications, can be a good alternative, both technically and financially. This study conducted a simulation based on density of the installation environment and configuration of the communications network to analyze the economic effect of installing a hybrid AMI communications network. It assumed that a hybrid AMI was deployed in an overhead distribution line in a low-density area. The simulation outcomes were compared and analyzed against the power line communication (PLC)-only AMI method. The results showed that the hybrid AMI method had a 10% communications network cost reduction effect compared to the PLC-only AMI method. In addition, the analysis indicated that there was a maximum 19% cost reduction effect in communications network installation depending on the method of network installation, suggesting that the hybrid AMI was economically more effective than the PLC-only AMI method.

Frequency regulation is essential for the stability of a power grid with high load fluctuation and integration of new energies. Constrained by the large ramping, a generator alone is not capable of conducting load frequency controls effectively and economically. In this paper, an energy storage system (ESS) is introduced to coordinate with generators in automatic generation control (AGC), where ESS and the generator respectively deal with high-frequency load fluctuation and low-portion. We develop a system configuration framework for such a hybrid system, including the operation strategy and capacity optimization. Due to the complexity of the hybrid system, the operation process is captured by a simulation model which considers practical constraints as well as remaining energy management of ESS. Taking advantage of the gradient-based approximation algorithm, we are then able to optimize the capacity of a hybrid system. According to the numerical experiments with real historical AGC data, the hybrid system is shown to perform well in cost reduction and to achieve the regulation tasks.

The irregularity and randomness of distributed energy sources’ (DERs) output power characteristic usually brings difficulties for grid analysis. In order to reliably and deterministically evaluate intermittent distributed generation’s active power output, a credible capacity index for active distribution network (ADN) is proposed. According to the definition, it is a certain interval that the stochastic active power output of DERs may fall in with larger probability in all kinds of possible dynamic and time varying operation scenarios. Based on the description and analysis on the time varying scenarios, multiple scenarios considered dynamic power flow method for and are proposed. The method to calculate and evaluate credible capacity based on dynamic power flow (DPF) result is illustrated. A study case of an active distribution network with DERs integrated and containing 32 nodes is selected; multiple operation scenarios with various fractal dimension are established and used. Results of calculated credible capacity based on several groups of scenarios have been analyzed, giving the variance analysis of groups of credible capacity values. A deterministic value with the maximum occurrence probability representing credible capacity is given. Based on the same network case, an application of credible capacity to grid extension planning is given, which contributes to expenditure and cost reduction. The effectiveness and significance of the proposed credible capacity and solution method have been demonstrated and verified.

Modern manufacturing systems should satisfy emerging needs related to sustainable development. The design of sustainable manufacturing systems can be valuably supported by simulation, traditionally employed mainly for time and cost reduction. In this paper, a multi-purpose digital simulation approach is proposed to deal with sustainable manufacturing systems design through Discrete Event Simulation (DES) and 3D digital human modelling. DES models integrated with data on power consumption of the manufacturing equipment are utilized to simulate different scenarios with the aim to improve productivity as well as energy efficiency, avoiding resource and energy waste. 3D simulation based on digital human modelling is employed to assess human factors issues related to ergonomics and safety of manufacturing systems. The approach is implemented for the sustainability enhancement of a real manufacturing cell of the aerospace industry, automated by robotic deburring. Alternative scenarios are proposed and simulated, obtaining a significant improvement in terms of energy efficiency (−87%) for the new deburring cell, and a reduction of energy consumption around −69% for the coordinate measuring machine, with high potential annual energy cost savings and increased energy efficiency. Moreover, the simulation-based ergonomic assessment of human operator postures allows 25% improvement of the workcell ergonomic index.

PV in combination with Li-ion storage systems can make a major contribution to the energy transition. However, large-scale application will only take place when the systems are economically viable. The profitability of such a system is not only influenced by the investment costs and economic framework conditions, but also by the technical parameters of the storage systems. The paper presents a methodology for the simulation and sizing of PV home storage systems that takes into account the efficiency of the storage systems (AC, DC standby consumption and peripheral consumption, battery efficiency and inverter efficiency), the aging of the components (cyclic and calendar battery aging and PV degradation), and the intelligence of the charging strategy. The developed methodology can be applied to all regions. In this paper, a sensitivity analysis of the influence of the mentioned technical parameters on the dimensioning and profitability of a PV home storage is performed. The calculation is done for Germany. Especially, battery aging, battery inverter efficiency and a charging strategy to avoid calendar aging have a decisive influence. While optimization of most other technical parameters only leads to a cost reduction of 1–3%, more efficient inverters can save up to 5%. Even higher cost reductions (more than 20%) can only be achieved using batteries that age less, especially batteries that are less sensitive to calendar aging. In individual cases, a small improvement in the efficiency of the storage system can also lead to higher costs. This is for example the case when smaller batteries are combined with a large PV system and the battery is used more due to the higher efficiency. This results in faster ageing and thus earlier replacement of the battery. In addition, the paper includes a detailed literature overview on PV home storage system sizing and simulation.

In order to tap the world wide offshore wind resources above deep waters, cost efficient floating platforms are inevitable. Tension-Leg Platforms (TLPs) could enable that crucial cost reduction in floating wind due to their smaller size and lighter weight compared to spars and semi-submersibles. The continuous development of the GICON®-TLP is driven by computer-aided engineering. So-called aero-hydro-servo-elastic coupled simulations are state-of-the-art for predicting loads and simulating the global system behavior for floating offshore wind turbines. Considering the complexity of such simulations, it is good scientific praxis to validate these numerical calculations by use of scaled model testing. This paper addresses the setup of the scaled model testing as carried out at the offshore basin of the École Centrale de Nantes, as well as the numerical model for the GICON®-TLP. The results of dedicated decay tests of the scaled model are used to validate the computational model at the first stage and to determine the natural frequencies of the system. Besides different challenges to the scaled model during the survey, it was possible to take these difficulties into account when updating the numerical model. The results show good agreements for the tank tests and the numerical model.

This paper analyses the emissions and cost impacts of mitigation of non-CO2 greenhouse gases (GHGs) at a global level, in scenarios aimed at meeting a range of long-term temperature goals (LTTGs). The study combines an integrated assessment model (TIAM-Grantham) representing CO2 emissions (and their mitigation) from the fossil fuel combustion and industrial sectors, coupled with a model covering non-CO2 emissions (GAINS), using the latest global warming potentials from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report. We illustrate that in general non-CO2 mitigation measures are less costly than CO2 mitigation measures, with the majority of their abatement potential achievable at US2005$100/tCO2e or less throughout the 21st century (compared to a marginal CO2 mitigation cost which is already greater than this by 2030 in the most stringent mitigation scenario). As a result, the total cumulative discounted cost over the period 2010–2100 (at a 5% discount rate) of limiting global average temperature change to 2.5 °C by 2100 is $48 trillion (about 1.6% of cumulative discounted GDP over the period 2010–2100) if only CO2 from the fossil fuel and industrial sectors is targeted, whereas the cost falls to $17 trillion (0.6% of GDP) by including non-CO2 GHG mitigation in the portfolio of options—a cost reduction of about 65%. The criticality of non-CO2 mitigation recommends further research, given its relatively less well-explored nature when compared to CO2 mitigation.

In this paper, a design driven comparison between two 190 kVA industrial three-phase two-level voltage source converter (2L-VSC) designs based in silicon carbide (SiC) and silicon (Si) for 690 V grids is presented. These two designs were conceived to have the same nominal power, while switching at reasonable switching speeds and requiring the same case to ambient thermal impedance. Under these conditions, the designs were studied to detect the potential gains and limitations that a pragmatic converter design could feature when using these two technologies regarding cost, efficiency, size and weight. To achieve this, experimentally determined semiconductor characteristics were used to perform simulations, the results of which were then used to design the essential parts of the converter. These designed parts were then corroborated with manufacturers, from which physical characteristics of all designed components were obtained. The results show that the SiC based design presents substantial weight savings and an 11% system cost reduction, while preserving its traditional characteristics such as improved overall efficiency when compared to the silicon based design under the given design requirements and constraints.

The energy efficiency gap is known as the difference between optimal level of energy efficiency and the actual level of achieved energy efficiency. Energy management has proven to further close the energy efficiency gap. Energy management may differ depending on whether it concerns a large, energy-intensive company or small and medium-sized enterprises (SMEs). SMEs are of high interest since they form a large share of the economy today. For SMEs, a lighter form of energy management, in the form of energy efficiency network participation, has proven to deliver sound energy efficiency impact, while for larger, energy-intensive firms, a certified energy management system may be more suitable. However, various barriers inhibit adoption of energy efficiency measures. While there is an array of research on barriers to and driving forces for energy efficiency in general, research on barriers to, and driving forces for, energy management is rare, one exception being a study of energy-intensive pulp and paper mills. This holds even more so for industrial SMEs. This paper aims to identify the barriers to, and drivers for, energy management in manufacturing SMEs. Results of this explorative study show that the top four barriers to energy management are lack of time/other priorities, non-energy-related working tasks are prioritized higher, slim organization, and lack of internal expert competences, i.e., mainly organizational barriers. The top four drivers for energy management are to reduce production waste, participation in energy efficiency networks, cost reduction from lower energy use, and commitment from top management. Furthermore, results show that energy management among the studied SMEs seems to not be as mature, even though the companies participated in an energy management capacity building program in the form of energy efficiency networks, which, in turn, shows a still largely untapped potential in the societal aim to reduce the energy efficiency and management gaps. The main contribution of this paper is a first novel attempt to explore barriers to, and drivers for, energy management among SMEs.

This paper assesses energy savings in terms of heating, cooling, and artificial lighting achieved in an office building with a high Window to Wall Ratio (WWR), located in Athens and Stockholm. Six different configurations of triple pane windows combining thermochromic, electrochromic, and low-e coatings in Insulated Glass Units (IGUs) are examined to quantify the potential of increasing energy savings in office buildings. The combination of electrochromic and thermochromic layers on the outer pane of the triple IGU, achieved an 18.5% and 8.1% reduction in annual primary energy use for Athens and Stockholm, respectively. The used switching strategy ensured the visual comfort of the employees and provided adequate daylight in both cases. It was found that in Stockholm the cooling system can be downsized considerably, achieving important cost reduction in the HVAC system. The analysis also takes into account the reduction of thermal storage in the building envelope due to the switchable windows. This reduction is beneficial in the case of Athens, but it can reduce energy savings in Stockholm. Therefore, for colder climates, it is important to increase window heat gains through the development of layers with higher g-value in the clear state and through the application of appropriate switching strategies.

The current study demonstrates the possibilities of reducing energy use and construction costs and provides evidence that wooden nearly-zero-energy buildings (nZEB) are technically possible at affordable construction costs by using novel design processes and procurement models that enable scalable and modular production. The energy efficiency solutions were derived by increasing/decreasing the insulation value of the building envelope in successive steps. Financial calculations were based on the investment needed to achieve the nearly-zero-energy levels. Overall, many opportunities exist to decrease the cost and energy use compared to the current (pre-nZEB) practice because the net present value can change up to 150 €/m² on the same energy performance indicator (EPI) level. The EPI in the cost-even range was reached by combining a ground-source heat pump (between 115 and 128 kWh/(m2·a)) and efficient district heating (between 106 and 124 kWh/(m2·a)). As energy efficiency decreases, improving energy efficiency becomes more expensive by insulation measures. Throughout the EPI range the most cost efficient was investment in the improvement of the thermal transmittance of windows (3–13 €/(kWh/(m2·a))) while investments in other building envelope parts were less effective (4–80 €/(kWh/(m2·a))). If these were possible to install, photovoltaic (PV) panels installed to the roof would be the cheapest solution to improve the energy performance. Integrated project delivery procurement (design and construction together) and the use of prefabricated wooden structures reduced the constructing cost by half (from ~2700 €/net m2 to 1390 €/net m2) and helped to keep the budget within limits.