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Abstract: Turkish electricity market law (EML) came into force in 2001 aiming at establishing a financially strong, stable, transparent and competitive electricity market based on bilateral contracts. Also, a balancing and settlement system (BSS) was put into practice in November 2004 to create a market where uncontracted generation can be traded, and actual implementation of the BSS started on August, 1st 2006 following a 21-month virtual implementation period. However, BSS has always been criticized from its beginning as transferring excessive profits to private generation companies. The present paper analyzes the implementation of BSS and argues that current BSS not only undermines the healthy development of the electricity market in Turkey but also prevents power investments due to uncertainties it created. It concludes that since the inconsistency between the objectives of EML and results of BSS in practice is obvious, Turkish policy makers need to modify current electricity market policy in line with suggestions presented in the paper. [Copyright &y& Elsevier]

One of the major concerns of electric power utilities in recent years is mitigating wildfire risks. While different mitigations have been proposed and some are implemented, customer experience is less focused in some methods, and not even considered in some extreme methods. This paper focuses on minimizing such tradeoff: reliability vs. wildfire risk reduction. This paper first proposes a methodology to quantify the wildfire risk raised by electric distribution systems, utilizing the vegetation factor at proper locations as well as the probability of fault. An optimization framework is then developed in this work that considers system reliability improvement in decision-making while reducing the risk of wildfires. The means to achieve such optimization are reclosers and fuses: optimally locate them to minimize the wildfire risk while the negative impact on reliability is minimized. Both fuse-blowing and saving schemes are investigated in this work. Moreover, the temporary and permanent types of faults are taken into account by formulating their impact on the operation of reclosers and fuses in both fuse-blowing and saving schemes. The optimization framework utilizes the exchange market algorithm (EMA) to find optimal results. The effectiveness of the proposed approach is verified by utilizing a modified IEEE 123 node system. • A proposed index models the wildfire risk associated with conductors and fuses. • A novel optimization framework is presented for recloser and fuse siting. • This model minimizes the wildfire risk and its impact on the system's reliability. • The optimization approach considers the recloser-fuse coordination practices. • This method provides different formulas for the fuse-saving and blowing schemes. [ABSTRACT FROM AUTHOR]

District heating networks offer promising solutions for remote communities, providing centralized heat supply, improved efficiency, and diverse energy sources, especially with existing diesel generation. Hence, this paper bridges gaps in the existing literature by developing comprehensive dynamic models of combined district heating networks within existing electric power networks in remote communities, which allows identifying challenges and benefits of district heating networks for these communities. It is shown that district heating networks allow utilizing waste energy to enable energy exchanges between the electricity and heating systems, enabling the provision of necessary ancillary services for remote microgrids with renewable energy sources. The presented dynamic district heating network model incorporates particular considerations in remote, northern communities such as soil limitations, extreme cold conditions, and piping insulation to minimize heat loss. It also addresses accurate sizing of heat pumps based on realistic thermal load requirements, weather conditions, and consumer profiles, proposing demand management controls to enhance frequency regulation for the integration of variable renewable energy sources. The main contributions of the paper include detailed dynamic modeling for district heating network operation, heat pump demand response control system design, and a comparative analysis between centralized district heating networks and decentralized electric thermal storage units that have been deployed for thermal supply in remote areas. The presented dynamic models are applied, tested, and validated in an existing electric microgrid at Kasabonika Lake First Nation in Northern Ontario, showcasing the role of a potential district heating network in facilitating renewable energy sources integration in isolated microgrids. • Use diesel generation exhaust for district heating in remote communities. • Use heat pump demand response for frequency control in remote microgrids. • Facilitate renewable energy integration using district heating networks in isolated microgrids. • Compare district heating networks with electric thermal storage units for thermal supply and frequency control in remote microgrids. [ABSTRACT FROM AUTHOR]

The carbon dioxide (CO 2) emissions have reached their highest levels since measurements began in recent years. This is widely regarded as one of the primary factors contributing to the increasing frequency of extreme climate events. Consequently, accurate and reliable CO 2 emission data becomes the benchmark in shaping policies and setting targets aimed at reducing emissions, which are essential for carbon emission-related research. Among all industries, the electric power sector takes the highest proportion of CO 2 emissions, making the carbon emission accounting for this sector a crucial component of overall carbon emissions accounting. Therefore, the objective of this paper is to provide a comprehensive review of the applied technologies and research progress in electricity carbon emissions accounting. The paper begins by introducing the fundamental concept of electricity carbon emission accounting. Electricity carbon emissions are categorized into two types: direct carbon emissions from electricity production and indirect carbon emissions from electricity consumption. Then, the current applied approaches for accounting for these two types of emissions are reviewed separately. After analyzing the shortages of existing applied carbon emission accounting approaches, the paper proceeds to review the research on the improved approaches of direct and indirect carbon emission accounting. Finally, the paper outlines the future development prospects of carbon emission accounting technology. • Application status of carbon emission accounting approaches in the electric power industry is analyzed. • Direct carbon emission accounting approaches based on big data technologies are introduced. • Research exploration in electricity indirect carbon emission accounting is reviewed and compared. [ABSTRACT FROM AUTHOR]

Solar photovoltaic generation has become the dominant global method of producing renewable electricity around the globe. However, solar PV farms require a considerable amount of land. Agrivoltaics has been a promising field of interest recently as this system maximizes the land's utilization by producing crops beneath the photovoltaics panels. This paper proposes a new agrivoltaics system that simultaneously produces crops and electrical power by installing PV panels on agricultural land in such a way that the production of regular crops does not get curtailed and can still grow around and beneath the panels, avoiding any reduction in crop yield. The architectural design of the proposed mounting structure and the installation method are discussed, ensuring full utilization of the land area under the panel with no crop limitations. Bangladesh is considered a case study location as its economy is mostly dependent on agriculture. The country started allocating enormous amounts of farmland for solar photovoltaic farms to mitigate the energy crisis. After a preliminary survey, an agrivoltaics system was designed, developed and installed in the Chuadanga District of Bangladesh. Then a detailed techno-economic analysis was performed to evaluate the feasibility and economic viability of the implemented agrivoltaics project. A comparative analysis of seven different scenarios is demonstrated in terms of equity payback, internal rate of return, modified internal rate of return, net present value, annual life cycle savings, and benefit-cost ratio to determine the optimum agrivoltaics approach as well as to showcase the superiority of the proposed system. The results demonstrate that the proposed agrivoltaics system achieves full land utilization, by producing crops along with electricity generation with the lowest payback period, highest profit margin, and highest benefit-cost ratio over the project lifetime. • An innovative Agrivoltaic (AV) approach for simultaneous food and energy production on the same land. • A technique to minimise the shading effect on the crop yield ensures full farmland utilization. • A quantitative techno-economic analysis for evaluating profit margin and energy balance. • Reduced payback period (3 years) compared to typical PV system (5–6 Years). [ABSTRACT FROM AUTHOR]

The recent international agreements signed by the majority of developed countries, such as those reached at the Climate Change Conferences in Paris'15 and Dubai'23, propose an increasingly rapid transition toward an energy ecosystem with a clear predominance of renewable energy in electrical power systems. The development of such ambitious energy programs should deal with the inherited stochasticity that renewable energy systems entail, which, when coupled with uncertainty about the capacity of the grid to maintain a power supply owing to increasing demands, decarbonization processes and the widespread closure of nuclear power plants, pose a serious threat to the normal functioning of energy systems. When combined with the escalation of armed conflicts that imply the loss of supply as a result of attacks or cyberattacks on power plants and distribution networks, it becomes clear that the current energy paradigm in which there is a centralized grid supplying numerous consumers, many of whom do not have their own generation capacity, must shift toward increasing the deployment of renewable-energy-based self-consumption facilities. The continuous advances toward a decentralized energy system of this nature will also lead to more cooperation, increasing the presence of energy communities with a great need to strengthen internal resilience as a sustaining factor. Considering the challenging framework of smart grids and energy transition, Microgrids would appear to be the key technology for the aggregation of generation, load and energy storage systems, and a cornerstone with which to provide the resilience and flexibility required for this new renewable-energy-based scenario. In addition to the complexity of the microgrid control problem, the issue of resilience energy management also has to be considered, which refers to the ability to adapt and supply loads during a specified period after a disruptive event with a loss of grid supply. This paper introduces an innovative method based on Model Predictive Control (MPC) techniques with the aim of enhancing resilience in microgrids by maximizing the energy surplus and reducing the aforementioned critical instants at which the capacity to feed loads is minimum in the case of power grid outages. The results obtained show that the proposed algorithm enhances the energy resilience in microgrids while the overall operational cost is optimized. A method with which to enhance the resilience of interconnected microgrids through cooperative optimization methods is also developed and validated. [Display omitted] • New techniques for resilience enhancement in microgrids/ networks of microgrids. • Optimizing energy management by enhancing surplus during critical resilience points. • Cooperative resilience management in networked microgrids with cost efficiency. [ABSTRACT FROM AUTHOR]

Highlights • We compare mobility cost of three different e-fuels, diesel, methanol, and LOHC. • The energy market design has a major influence on the production costs of e-fuels. • Dynamic and price-sensitive production of e-fuels can make them competitive in 2035. • LOHC is the most cost-effective option compared to synthetic diesel or methanol. • The optimal setup of e-fuel production is decisively affected by the market design. Abstract Electric fuels (e-fuels) enable CO 2 -neutral mobility and are therefore an alternative to battery-powered electric vehicles. This paper compares the cost-effectiveness of Fischer-Tropsch diesel, methanol and Liquid Organic Hydrogen Carriers. The production costs of those fuels are to a large part driven by the energy-intensive electrolytic hydrogen production. In this paper, we apply a multi-level electricity market model to calculate future hourly electricity prices for various electricity market designs in Germany for the year 2035. We then assess the economic efficiency of the different fuels under various future market conditions. In particular, we use the electricity price vectors derived from an electricity market model calibrated for 2035 as an input for a mathematical model of the entire process chain from hydrogen production and chemical bonding to the energetic utilization of the fuels in a vehicle. Within this model, we perform a sensitivity analysis, which quantifies the impact of various parameters on the fuel production cost. Most importantly, we consider prices resulting from own model calculations for different energy market designs, the investment cost for the electrolysis systems and the carbon dioxide purchase price. The results suggest that the use of hydrogen, which is temporarily bound to Liquid Organic Hydrogen Carriers, is a favorable alternative to the more widely discussed synthetic diesel and methanol. [ABSTRACT FROM AUTHOR]

Highlights • It describes the motivation of a cluster approach and defines its associated boundary dimensions. • A set of influencing factors on urban energy systems at cluster level are identified. • Three main categories of RES envelope solutions are summarized. • Modelling techniques for energy systems at cluster level are reviewed. Abstract The emergence of renewable-energy-source (RES) envelope solutions, building retrofit requirements and advanced energy technologies brought about challenges to the existing paradigm of urban energy systems. It is envisioned that the building cluster approach—that can maximize the synergies of RES harvesting, building performance, and distributed energy management—will deliver the breakthrough to these challenges. Thus, this paper aims to critically review urban energy systems at the cluster level that incorporate building integrated RES solutions. We begin with defining cluster approach and the associated boundaries. Several factors influencing energy planning at cluster scale are identified, while the most important ones are discussed in detail. The closely reviewed factors include RES envelope solutions, solar energy potential, density of buildings, energy demand, integrated cluster-scale energy systems and energy hub. The examined categories of RES envelope solutions are (i) the solar power, (ii) the solar thermal and (iii) the energy-efficient ones, out of which solar energy is the most prevalent RES. As a result, methods assessing the solar energy potentials of building envelopes are reviewed in detail. Building density and the associated energy use are also identified as key factors since they affect the type and the energy harvesting potentials of RES envelopes. Modelling techniques for building energy demand at cluster level and their coupling with complex integrated energy systems or an energy hub are reviewed in a comprehensive way. In addition, the paper discusses control and operational methods as well as related optimization algorithms for the energy hub concept. Based on the findings of the review, we put forward a matrix of recommendations for cluster-level energy system simulations aiming to maximize the direct and indirect benefits of RES envelope solutions. By reviewing key factors and modelling approaches for characterizing RES-envelope-solutions-based urban energy systems at cluster level, this paper hopes to foster the transition towards more sustainable urban energy systems. [ABSTRACT FROM AUTHOR]

Graphical abstract In exploring China's optimal power generation mix, we employ scenario analysis to investigate the influence of cost, risk factors, technological development restrictions and different policy targets. Under the technological realizable case, the risk minimization portfolio remains unchanged while the cost and risk of cost minimization portfolio increase when the policy target package improves. Highlights • China's optimal power generation mix of 2030 is explored using portfolio approach. • Grid availability is used to reflect the risk for non-fossil generation technologies. • Fossil fuel forms have no priorities when considering technological restrictions. • Positive effects exist for a non-fossil generation goal or a policy target package. Abstract This paper employs portfolio theory to explore China's optimal power generation mix in 2030, which considers the possibilities of technological developments, and the government's non-fossil generation and non-hydro renewable generation policy targets. The aim of this paper is to investigate China's efficient generation portfolios by comparing the portfolio costs, risks, efficient frontiers, and diversification levels under different cases and scenarios. The results show that fossil fuel generation technologies have no advantages when considering the technological restrictions. In addition, various preferences for non-fossil generation technologies are influenced greatly by the goals of pursuing cost or risk minimization and different policy targets. Positive effects exist for a non-fossil generation goal or a policy target package from the perspectives of minimizing cost and risk, as well as for improving the diversification level. [ABSTRACT FROM AUTHOR]

• The optimization problem of EPS and flexibility-enhanced DHSis formulated. • The DHS is retrofitted with BCT and uses a variable mass flow operation strategy. • A conditional-DRO is employed to hedge against the uncertainty of renewable energy. • Propose adouble-layer distributed algorithm to achieve the distributed solution. In an integrated electricity and heating system (IEHS), building flexibility-enhanced district heating systems (DHSs) that use advanced flexibility-enhanced technologies are effective for promoting renewable energy accommodation. However, it is challenging to comprehensively utilize these flexibility-enhanced technologies in IEHS dispatch. This paper proposes a distributed conditional-distributionally robust coordination method for an electrical power and flexibility-enhanced DHS. The DHS is retrofitted with bypass compensation technology and uses a variable mass flow operation strategy to enhance its flexibility and promote renewable energy accommodation. Moreover, conditional-distributionally robust optimization is used to consider the inherent dependency between the forecasting error of a wind farm and the forecast output information, which goes beyond the state-of-the-art models. Additionally, a novel reformulation method is adopted to transform the problem with random variables and bilinear constraints into a more tractable programming problem, yielding a conservative approximative model of the original model that guarantees feasibility. Finally, to protect the privacy of electrical power system and DHS operators, this paper proposes a double-layer distributed algorithm to solve the problem in a distributed fashion while guaranteeing feasibility. Case studies demonstrate that the proposed model with flexibility-enhanced DHSs significantly promotes renewable energy accommodation and reduces dispatch costs, and the proposed double-layer distributed algorithm effectively solves the dispatch problem in a distributed fashion and guarantees the feasibility of dispatch results. [ABSTRACT FROM AUTHOR]

The high variability and uncertainty introduced into modern electrical distribution systems due to decentralized renewable energy generators requires new solutions for grid management and power quality assurance. One of these possible solutions includes grid integrated energy storage. The appropriate size and placement of decentralized storage is highly dependent on purpose of the battery system and expected operational strategy. However, battery operational strategies are difficult to simulate simultaneously during a sizing and placement planning calculation. The motivation of this paper is to propose an algorithm that is capable of integrating sizing, placement and operational strategies of batteries into an Optimal Power Flow (OPF) distribution grid planning tool. The choice of the OPF approach permits to account for grid constraints which is more adapted for grid-connected storage devices compared to other approaches in the state of the art that are based only on an email balance analysis. This paper presents an alternating current (AC) multi-temporal OPF algorithm that uses a convex relaxation of the power flow equations to guarantee exact and optimal solutions with high algorithmic performance. The algorithm is unique and innovative due to the fact that it combines the simultaneous optimization of placement and sizing of storage devices taking into account load curves, photovoltaic (PV) production profiles, and distribution grid power quality constraints. The choice to invest in battery capacity is highly sensitive to the price of battery systems. The investment in battery systems solely for reducing losses an operational costs was proven not to be cost effective, however when battery systems are allowed to buy and sell electricity based on variable market prices they become cost effective. The assumptions used for this study shows that current battery system prices are too high to be cost effective even when allowing battery system market participation. [ABSTRACT FROM AUTHOR]

Traditional energy planning methods are mostly related to newly-built area. However, in most already-built regions, the existing district energy systems (DESs) need to be ameliorated due to insufficient capacity and low efficiency. This paper investigates the methodology of expansion planning, which can offer planners a viable way to expand existing DESs. The term “expansion” aims to increase the supply capacity of the energy forms, e.g., steam, hot water and electricity, which cannot be accessed from national or urban energy supply network, and meanwhile increase energy utilization efficiency and cut CO 2 emission. Three parts of the methodology, including problem analysis in current DESs, demand side analysis and the existing DES expansion, are introduced in this paper. New concepts of load structure and evaluation system of the whole district are proposed in this methodology. A case analysis of Ningbo Hi-Tech District, China is conducted to shed light on the application of this method. [ABSTRACT FROM AUTHOR]

Hydrogen propulsion is generally treated as the ultimate net zero-emission option for aviation, therein liquid hydrogen storage appears to provide the only feasible solution for aircraft with the constraints imposed by aircraft weight and volume. Hydrogen liquefaction consumes a great amount of energy, which significantly boosts the cost of hydrogen as an aviation fuel. Besides the H 2 chemical exergy for combustion to provide thrust, the H 2 physical exergy in the form of cold energy is also a kind of high-quality clean energy to produce power output, which offers great opportunities to achieve cost-effective use of hydrogen fuel. Thus, this paper presents a Rankine cycle and H 2 direct expansion cycle (RC-DEC) combined power generation thermal management system for the simultaneous utilization of exhaust gas heat exergy and hydrogen fuel cold exergy from the liquid hydrogen-fueled turbojet engine. In the Rankine cycle, the transcritical mode is applied on the hot side while the liquid separation condensation concept is adopted on the cold side to achieve better thermal matchings with heat and cold sources. The hydrocarbon/inert gas binary mixture is proposed as the working fluid of the Rankine cycle. The effects of key thermodynamic parameters and the influence of Para-Ortho hydrogen conversion on system performance are discussed, then a comparison of the mixture working fluid is conducted. Results show that there exists an optimal separation temperature ratio and condensation pressure of RC to obtain maximum total net power output. As the DEC expander inlet pressure increases, the total net power output first increases and then levels off. The introduction of the Para-Ortho hydrogen conversion has a negligible impact on net power output but leads to a significant reduction in the exergy efficiency, which is attributed to the cold energy release nature of Para-Ortho hydrogen conversion. The results of the working fluid comparison indicate that the optimal inert gas retardant for CH 4 is Kr, while those for C 2 H 6 and C 3 H 8 are Xe to achieve the maximum net power output and exergy efficiency of the proposed power generation system. Among them, C 2 H 6 /Xe(0.6/0.4) obtains the maximum net power output of 2717.9 kW and maximum exergy efficiency of 33.1%, which suggests an opportunity to provide megawatts of electrical power for the aircraft in place of the APU. • RC-DEC combined thermal management system is proposed for LH 2 turbojet engine. • The hydrocarbon/inert gas binary mixture is proposed for LH 2 cold exergy recovery. • The influence of the Para-Ortho hydrogen conversion on the system is revealed. • The optimal inert gas retardant for the selected hydrocarbons is determined. [ABSTRACT FROM AUTHOR]

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers' financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid's low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers' distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms. • Data Empowered Appliance-level Utility Modeling. • Demand Response Design to Offer Appliance-level Incentive. • Heterogeneity Characterization to Improve Scalability and Efficiency. [ABSTRACT FROM AUTHOR]

The recent trend to design more efficient and versatile ships has increased the variety in hybrid propulsion and power supply architectures. In order to improve performance with these architectures, intelligent control strategies are required, while mostly conventional control strategies are applied currently. First, this paper classifies ship propulsion topologies into mechanical, electrical and hybrid propulsion, and power supply topologies into combustion, electrochemical, stored and hybrid power supply. Then, we review developments in propulsion and power supply systems and their control strategies, to subsequently discuss opportunities and challenges for these systems and the associated control. We conclude that hybrid architectures with advanced control strategies can reduce fuel consumption and emissions up to 10–35%, while improving noise, maintainability, manoeuvrability and comfort. Subsequently, the paper summarises the benefits and drawbacks, and trends in application of propulsion and power supply technologies, and it reviews the applicability and benefits of promising advanced control strategies. Finally, the paper analyses which control strategies can improve performance of hybrid systems for future smart and autonomous ships and concludes that a combination of torque, angle of attack, and Model Predictive Control with dynamic settings could improve performance of future smart and more autonomous ships. [ABSTRACT FROM AUTHOR]

The common semi-empirical modelling adopted for positive-displacement rotary expanders is revised in this paper. Paying particular attention to the leakage flow rates, the mechanical losses at the shaft and the ambient heat losses by the proposal of a more physically sound modelling, this paper aims at improving the performance simulation of a single-screw expander for which there exists a wide experimental campaign in literature. In detail, the mechanical losses are modelled with an approach consistent with the Stribeck’s theory, whereas the contributions of both natural convection and radiation are taken into account for a proper modelling of the ambient heat losses. After calibration and validation of the modelling procedure, based on experimental data of the expander operation with R245fa, mean absolute percentage errors of 0.69%, 1.77% and 0.33% as regards mass flow rate, electric power output and exhaust fluid temperature, respectively, are calculated. These errors are lower than the ones reported by other researchers, so the current simulations are more consistent with the experimental data. Considering the higher reliability for a better performance simulation by the new modelling procedure, the model is finally used to study the behavior of the expander. In particular, attention is paid to the mass flow rate, the shaft and the electric power outputs, the expander efficiency, as well as the ambient heat losses, and to their relations with the operation parameters such as the degree of fluid superheat at the expander inlet, the fluid pressure at the expander inlet, the pressure ratio and the rotational speed. [ABSTRACT FROM AUTHOR]

In this paper, we develop a mathematical model to analyze a direct ethanol fuel cell (DEFC). The three-dimensional model is able to predict the flow on all layers of the fuel cell and allow a better analysis of physical and chemical phenomena that occur inside it. In addition, the calculation of mole fraction of species allows one to observe that the diffusion layer has great influence on mass transfer of fuel between the input channel and the catalyst layer. Numerical simulation of reactive flow was made based on the central finite difference method. The equations were integrated in time using the simplified Runge-Kutta multistage scheme. The results obtained are in agreement with the experimental data found in the literature, for different feed concentrations of ethanol and for different operating temperatures of the cell. In this way, the paper contributes to the development of a model for direct ethanol fuel cells, taking into account all losses overpotentials at the anode and the cathode, providing a better understanding of the physical and chemical behavior inside the cell, and on the conversion of chemical energy into electrical energy. [ABSTRACT FROM AUTHOR]

This paper presents a detailed analysis of the levelized cost of storage (LCOS) for different electricity storage technologies. Costs were analyzed for a long-term storage system (100 MW power and 70 GWh capacity) and a short-term storage system (100 MW power and 400 MWh capacity). Detailed data sets for the latest costs of four technology groups are provided in this paper. The LCOS method allows a cost comparison of technologies in different system designs and various operation modes. The results for the long-term storage show that Pumped-Storage Hydroelectricity has the lowest LCOS among the mature technologies today. Power to Gas technologies, once established on the market, may also provide long-term electricity storage at even lower LCOS. Pumped-Storage Hydroelectricity is also the cheapest technology for short-term storage systems. Battery systems at the moment still have high costs but are expected to have a sharp price decrease in the near future. Power to Gas and adiabatic Compressed Air Energy Storage systems may become cost competitive as short-term storage systems as well. The detailed analysis of the cost components shows that the cost composition is very inhomogeneous among the technologies. Plant design optimized to the application is therefore crucial for cost minimization. Sensitivity analysis shows that for most technologies the amount of energy discharged as well as the cost of electricity purchase are the most influential factors for the LCOS. [ABSTRACT FROM AUTHOR]

Biogas production and its derived hydrogen production technology have broad application prospects. In this paper, an integrated biogas power generation system with solid oxide fuel cells is proposed, which mainly consists of four units: a solar thermal energy storage unit, a biogas production and hydrogen generation unit, a SOFC-MGT unit, and a waste heat utilization unit. The presented system is first studied using energy, exergy, economic, and life cycle environmental analyses and the survey results are contrasted with those of renewable energy systems discussed in the references. Besides, a parametric study is conducted to explore the effect of thermodynamic parameters of solar irradiance, ambient temperature, work fluid temperature, reforming temperature, inlet H 2 O/CH 4 ratio of the reformer, and the current density of SOFC. Results indicate that the proposed system's energy efficiency, exergy efficiency, electric power output and exergy destruction are 43.29%, 37.4%, 414.16 kW (net power generation is 351.43 kW) and 1434.59 kW, respectivily. Moreover, the system's levelized cost of electricity and carbon emission factor are $0.076/kWh and 335.6gCO 2 eq/kWh, having good economic and environmental benefits simultaneously. The parametric study shows that PTSC can be designed shorter with more solar irradiance and high ambient temperature. Besides, the increase of inlet H 2 O/CH 4 ratio and reforming temperature make the system's energy efficiency fall by 2.5% and 20%. For every 300 A/m2 increase in the current density of SOFC, the system efficiency rises by 1.01%. • A novel biogas-fueled solid oxide fuel cell hybrid power system assisted with solar thermal energy storage is designed. • The energy, exergy, economic, life cycle environmental analyses of the proposed system are carried out. • The influence of key parameters on system performance is discussed. • Performance of the proposed system is compared with similar systems in literature. • Solar thermal energy storage unit improves the system's adaptability to cold climates at high altitudes. [ABSTRACT FROM AUTHOR]

• Green hydrogen generation devices were studied. • Methods for annual operation evaluation were proposed. • Experimental study on transient states was conducted. • Case study for 10 MW renewable energy plant was considered. This study presents a comprehensive methodology to evaluate plants that integrate renewable energy sources and hydrogen generation devices. The paper focuses on presenting the methods for devices' operation assessment taking into account the annual operation. Multiple effectiveness indices have been presented. On the basis of experimental investigation with the hydrogen generator, the methods for assessing its operation during start-up phase and sudden change in the supply current were proposed. The results of the experiments and the provided mathematical models show that dynamics of the hydrogen generator should be taken into account when selecting the suitable device for cooperation with variable renewable energy. It is especially important for multiple start-ups throughout the day due to significant differences in the amount of hydrogen produced by devices characterized by the same efficiency, yet various time constants. Methodology for selecting the optimal nominal power for hydrogen generator to cooperate with given renewable sources was developed. It was proven the optimal power depends on the type of the renewable source and minimal load of the hydrogen generator. Several case studies, including the integration of wind and solar energy farms to yield a 10 MW renewable energy farm were considered and the minimal load of the hydrogen generator impacts the annual operation of the device has been presented. The paper provides a set of tools to contribute to the development of sustainable energy plants. The methods proposed in this paper are universal and can be used for various renewable energy sources. [ABSTRACT FROM AUTHOR]

China will enact a national emissions trading system in 2017. The rules for the initial allocation of emission allowances have not been settled yet. This paper assesses the effect of auctioning as an initial emission allowance allocation methods of the China electricity power generation sector. This study employs a combined model with modified Trans-log production function, dynamic simulation model and multi-objective linear programming to provide more realistic results. After estimating the optimal electric power sources structure and technological structure under different auctioning rates, this paper calculates the abatement cost of the China’s electric power generation sector. When the allowance is free, the carbon shadow price will be 206.12 Yuan/ton. When 5% allowance is auctioned, the price will be 216.91 Yuan/ton. When the auctioning rate increases, the carbon emission abatement cost increases accordingly, as does the growth rate. The results also show that when the allowance auction rate is 5%, the marginal abatement costs for coal-fired power generation and clean power generation are 0.123 Yuan/kW h and 0.121 Yuan/kW h respectively, and thus, the total additional cost of the electric power generation sector will increase by 0.244 Yuan/kW h, which would be a heavy burden to China’s electric power generation sector and the forward progress of auctioning in the near future may be hindered. [ABSTRACT FROM AUTHOR]

Thermoelectric generators (TEGs) present good potential applications for the conversion of low level thermal energy into electrical power, especially applied to waste heat recovery systems. This paper presents an advanced mathematical model that uses multi-element thermoelectric components connected in series, and considers internal and external irreversibility and also the temperature gradients along the flow direction. A Fortran computation program was used for the numerical calculations. This paper focusses mainly on the thermoelectric performance study under four different types of cooling methods with temperature gradient modeling. The results showed that it is important in TEG system design to choose an optimal module area that is appropriate to the mass flow rate of the fluids, but this is not affected by the intake temperature of fluids. With the water cooling method, the coflow and counterflow methods did not need to be distinguished because of their small difference, but they should be distinguished for the air cooling method. The counterflow arrangement generally produced a small higher maximum power output, but needed a much larger module area compared to the coflow method. [ABSTRACT FROM AUTHOR]

Waste heat recovery and utilization represents a missed opportunity to reduce China’s total energy use, decrease carbon dioxide emissions, and improve air quality. Currently, China does not have a standardized or transparent methodology to quantify the waste heat potential in the industrial sector, which accounts for more than two thirds of China’s primary energy consumption. This paper presents the results of thermal energy modeling to quantify the technical maximum waste heat potential in three energy-intensive industrial sectors: cement, iron and steel, and glass. In addition, this paper identifies the practical potential for producing electricity from waste heat in these sectors. The analysis finds that the glass sector has the highest waste heat to power generation potential per unit of production basis among the studied sectors. This paper provides key principles for managing waste heat in the industrial sector and key sector characteristics for implementing waste heat to power generation technologies. [ABSTRACT FROM AUTHOR]

The hypothesis of this paper is that in order to identify least cost solutions of the integration of fluctuating renewable energy sources into current or future 100% renewable energy supplies one has to take a Smart Energy Systems approach. This paper outline why and how to do so. Traditionally, significant focus is put on the electricity sector alone to solve the renewable energy integration puzzle. Smart grid research traditionally focuses on ICT, smart meters, electricity storage technologies, and local (electric) smart grids. In contrast, the Smart Energy System focuses on merging the electricity, heating and transport sectors, in combination with various intra-hour, hourly, daily, seasonal and biannual storage options, to create the flexibility necessary to integrate large penetrations of fluctuating renewable energy. However, in this paper we present the development and design of coherent Smart Energy Systems as an integrated part of achieving future 100% renewable energy and transport solutions. The transition from fossil fuels towards the integration of more and more renewable energy requires rethinking and redesigning the energy system both on the generation and consumption side. To enable this, the Smart Energy System must have a number of appropriate infrastructures for the different sectors of the energy system, which are smart electricity grids, smart thermal grids (district heating and cooling), smart gas grids and other fuel infrastructures. It enables fluctuating renewable energy (such as wind, solar, wave power and low value heat sources) to utilise new sources of flexibility such as solid, gaseous, and liquid fuel storage, thermal storage and heat pumps and battery electric vehicles. Smart Energy Systems also enable a more sustainable and feasible use of bioenergy than the current types allow. It can potentially pave the way to a bioenergy-free 100% renewable energy and transport system. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to design energy consumption modeling systems to predict the energy consumption value due to today's energy use in cities increasing. A new Modified Fish Migration Optimization Algorithm-based Numerical Moment Matching (MFMOA-NMM) technique is applied as an initial indeterminacy evaluation method to forecast the electrical power use in large-scale datasets of single-family residential homes with the application of main characteristics. To develop energy modeling, the DesignBuilder is used. The energy performance of the homes is assessed by combining these techniques. With the application of the information from the energy audit and data of the assessor (8368 single-family homes), four effective parameters including attic insulation R-value, the efficiency of air conditioners (SEER), type of the window, and the area of homes are inputted into the model improvement. Waterloo in Iowa is selected as the case study. The results showed that the yearly electrical power use expected is equal to 10,195 kWh which is within 5% of the experimental electrical power use. Also, the monthly expected electrical power use Average Bias Error is equal to 2.4% and the Coefficient of Variation with the Root Average Square Error is equal to 6.7%. This technique can be applied to create a small group of typical buildings to show the energy performance of a bigger group of buildings. • Energy consumption modeling to predict energy consumption for single-family homes in Waterloo. • Energy performance optimization by a Modified Fish Migration Optimization Algorithm. • Combining the introduced new algorithm with Numerical Moment Matching technique. [ABSTRACT FROM AUTHOR]

International Energy Agency predicts that the global number of installed heat pumps (HP) will increase from 180 million in 2020 to approximately 600 million by 2030, covering 20% of buildings heating needs. Electric power consumption is one of the main key performance indicators for the heat pump systems from techno-economic perspective. However a common issue prevalent in many existing heat pumps is the lack of electric power measurement. The modern installations might be equipped with electric power measurement sensors but this comes at a higher system cost for the manufacturers and end-users. The primary objective of this work is to propose a virtual measurement for estimating power consumption, thereby eliminating the need for field measurement of power for heat pumps. To achieve the objective, a data-driven approach is proposed. Firstly, the in-situ data is preprocessed through data merging, cleaning, and normalization. Then, input features are pre-selected using Spearman correlation coefficients, and further refined by addressing multicollinearity problem. Following this, Extreme Gradient Boosting (XGBoost) models and polynomial models are developed by considering different features as inputs. All models are finally validated against the in-situ data from multi-units of ground source heat pump (GSHP) and air source heat pump (ASHP) installations. The results showed that the electric power consumption of GSHP can be estimated with high accuracy (99% for R 2 , 10 W for MAE, and 1% for MAPE) through generic data-driven models using only four easy-to-measure input features. Taking three input features as inputs for ASHP generic model, the accuracy can be reached to 83% for R 2 , 125 W for MAE, and 9% for MAPE. The method presented in this paper can be applied to estimate power consumption of millions of heat pumps and consequently add a significant value as well as provide different types of services, such as cost-saving benefits for manufacturers and end-users, flexibility services for aggregators and electricity grids. • Collect and analyze in-situ data from multiple residential GSHP and ASHP units. • Propose a two-step feature selection method to indicate the most essential inputs. • Evaluate the most predominant features by data-driven models with different inputs. • Develop high accuracy generic data-driven models with limited inputs features. [ABSTRACT FROM AUTHOR]

A major difficulty in integrating large scale wind power generation in an electrical power system is that wind generated power appears to be erratic, intermittent, and volatile. In this paper, we demonstrate the efficacies of a novel ultra short term 1-step ahead wind generated power prediction model, by combining two best of breed machine learning models in their respective areas of applications: a deep convolutional neural network (CNN) model, known to be effective in classification problems, and a bi-directional long short term memory (Bi-LSTM) model, known to be effective in 1-step ahead time series prediction problems, using a cross attention (XA) mechanism on three challenging practical datasets: the East-China dataset, the Yalova (Turkey) dataset, and the 16 MW dataset. There are two alternative cross attention models: (1) using the CNN features as the key–value pair, and the Bi-LSTM features as the query, this we called a XA model, and (2) using the Bi-LSTM features as the key–value pair, and the CNN features as the query, this we called a XA_I model; we found empirically for a properly chosen sliding window length, whichever CNN model or Bi-LSTM model performs well in the 1-step ahead prediction, then its features should be used as the query in the cross attention mechanism, thus, the XA achieves better results than XA_I on the East-China dataset, while XA_I achieves better results than XA on the Yalova dataset, and on the 16 MW dataset. Where comparable results using other state-of-the-art models are available on the Yalova dataset and the 16 MW dataset, we found that our XA_I results are superior. Moreover, it is found empirically, that the cross attention mechanism model produces stable k -step ahead predictions, for k ≤ W 2 , where W is the sliding window length. • A novel prediction model for wind generated power based on cross attention mechanism has been proposed. • The proposed method demonstrated on three practical datasets to have superior performance. • The cross attention mechanism based models give a stable multi-step ahead prediction. [ABSTRACT FROM AUTHOR]

This paper presents the unique design and processing conditions of a micro-CAES device that generates, stores and delivers electrical energy with highly efficient total energy conversion. The micro-CAES installation is divided into six parts, where air, the energy carrier, is compressed, stored, heated and expanded in appropriate sections. We focus mainly on the post-preparation section, where the air mass flow is pulse heated and controlled by Pulse Width Modulation (PWM), and the expansion section, where multiple piston drives are used to achieve maximum energy conversion efficiency or the required amount of electricity to be fed into the grid. We discover that the controllability of expansion process performance is improved by exploiting the inertia of the following elements in multiple reciprocating engines: piston, piston rod, connecting rod, crank, and crankshaft, connected with air injection dynamics and pulse heating by Thermal Energy Storage (TES). Furthermore, we investigate the efficiency using our own zero-dimensional mathematical model illustrating the complex dynamics of air processing. The mathematical model was compared with experimental data for the single-drive air expander, showing qualitatively and quantitatively satisfying model reliability. Moreover, we performed a sensitivity analysis to determine the best number of reciprocating drives involved in the expansion section and the best operating conditions. The use of the PWM function of mass flow of air supplied the expander with an index of 0.5 together with pulsating air heating in the expander installation, including three- reciprocating drives, allows a maximum total energy conversion efficiency of 81%. The presented piston drive expander seems to be a promising solution for micro-CAES in residential and industrial applications, especially where additional low-temperature waste heat is available, and an intermittent control strategy is implemented. [Display omitted] • Original construction of the air expander includes multi-piston-drives. • The micro-CAES with air preheat and PWM control strategy ware implemented. • Obtained maximum efficiency of piston-drive micro-CAES is 81%. • Generated electrical power could be control with overall efficiency change up to 10%. [ABSTRACT FROM AUTHOR]

The growing frequencies of extreme weather events and cyber-attacks give rise to a novel threat where a malicious cyber actor aims to disrupt stressed components of critical infrastructure systems immediately before, during, or shortly after an extreme weather event. In this paper, we initiate the study of Compound Cyber–Physical Threats and develop a two-stage framework for the analysis of operational disruptions in electric power networks and economy-wide impacts under three scenarios: a Heatwave, a Cyber-attack, and a Compound scenario when the Cyber-attack is timed with the Heatwave. In the first stage, we use a bilevel optimization problem to represent the adversarial rationale of a cyber-attacker in the upper level. In the lower level, we model disruptions in the electric power network using an optimal power flow model. In the second stage, we couple the disruption of electricity supply with a Computable General Equilibrium model to elucidate the impacts on all economic sectors. For the New York Independent System Operator, we find that a 9% demand increase in a Heatwave may not lead to unserved load. The cyber-attack can lead to 4% of unserved electric load in Long Island, while the Compound scenario can increase unserved electric load in Long Island to 12% and affect almost 198,000 customers. Our results show that the activity of state and local government enterprises can decrease by 37% in the Compound scenario. We conclude that the vulnerability of federal, state, and local government enterprises to electricity disruptions can affect a broad range of populations. • Initiate the study of compound cyber-attacks and extreme weather events. • A compound cyber–physical threat can exacerbate regional electricity disruptions by 3 times. • Introduce coupling of cyber-attacker strategies with response of all economic sectors. • A compound cyber–physical threat can exacerbate economy-wide losses by 10 times. [ABSTRACT FROM AUTHOR]

This paper proposes a cyber–physical cooperative mitigation framework to enhance power systems resilience against power outages caused by extreme events, e.g., earthquakes and hurricanes. Extreme events can simultaneously damage the physical-layer electric power infrastructure and the cyber-layer communication facilities. Microgrid (MG) has been widely recognised as an effective physical-layer response to such events, however, the mitigation strategy in the cyber lay is yet to be fully investigated. Therefore, this paper proposes a resilience-oriented centralised-to-decentralised framework to maintain the power supply of critical loads such as hospitals, data centres, etc., under extreme events. For the resilient control, controller-to-controller (C2C) wireless network is utilised to form the emergency regional communication when centralised base station being compromised. Owing to the limited reliable bandwidth that reserved as a backup, the inevitable delays are dynamically minimised and used to guide the design of a discrete-time distributed control algorithm to maintain post-event power supply. The effectiveness of the cooperative cyber–physical mitigation framework is demonstrated through extensive simulations in MATLAB/Simulink. [Display omitted] • A centralised-to-decentralised framework is proposed to tackle adverse cyber–physical effects (ACPEs) under extreme events. • In the cyber layer, a dynamic resource allocation, over a limited wireless bandwidth, effectively and quantitatively optimises the non-negligible delays and informs the design of the control algorithm. • A discrete-time distributed control system based on optimised distributed communication network simplifies the selection of control gains. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an analysis aimed to evaluate the feasibility of micro-CHP systems to meet the household energy demands of single family users. The considered CHP systems, with an electric power output of a few kW, are based on technologies (internal combustion engines, micro gas turbines, micro Rankine cycles, Stirling engines and thermophotovoltaic generators) which are already available on the market or will be available in the near future. Each CHP system is composed of (i) a prime mover, (ii) a thermal energy storage unit and (iii) an auxiliary boiler used to cover peak thermal demands. The analyses performed in this paper aim to evaluate (i) the energy performance of the CHP systems to meet the energy demand of two single-family dwellings, with different floor areas and shape factors, and (ii) the maximum cost allowed for each CHP system. The results presented in this paper allow the most suitable technology, prime mover and thermal energy storage unit size to be identified. From an energy point of view, the considered CHP systems usually satisfy most of the thermal and electric energy demand, with a primary energy saving index that is always higher than 20%. Moreover, the correct sizing of the thermal energy storage unit capacity proves crucial. As regards economic feasibility, it is shown that a reasonable target for the marginal cost of a CHP system for household heating is approximately 3000€/kWe, even though the absence of incentives does not make any of the technologies very attractive at present. It is also shown that the highest profitability can be obtained by using the prime mover (i) with the lowest electric power output closest to that of the peak electric demand and (ii) with an electric-to-thermal ratio which fits the electric-to-thermal ratio of the users. [Copyright &y& Elsevier]

Abstract: This paper reports on a series of steam injection experiments on a Turbec T100 microturbine. Combined Heat and Power (CHP) systems, such as the considered T100 microturbine, use one single primary fuel to simultaneously produce electric and thermal power. In doing so, they realize significant energy savings compared to conventional schemes of separated production. However, a reduction in the demand for heat (e.g. in summertime) will force this type of units to shutdown. This significantly reduces the amount of operating hours and has a severe negative impact on the net present value of such CHP investment projects. The aim of this paper is to investigate and demonstrate the effects of steam injection in the compressor outlet of a microturbine operating under reduced heat demand conditions, in order to keep the unit running. The necessary steam can be auto-raised with heat available in the turbine exhaust downstream of the recuperator. Such an injection will keep the unit running and thus avoid a forced shutdown. Furthermore, it is expected that the electric efficiency will rise and that the power production will become more economically viable as a result of the increasing operating hours. This paper reports on the influence of steam injection on the electrical efficiency and shaft speed of a T100 unit. ASPEN® simulations of the behavior of the CHP unit are also presented. These simulations predicted a 2.2% rise in electric efficiency at nominal electrical output when 5% of the mass flow rate of air is replaced by steam. The steam injection experiments resulted in stable runs of the unit, a predicted reduction in shaft speed and increasing electrical efficiency. Validation of the ASPEN® simulations against the experimental data revealed the necessity for a more accurate determination of the air mass flow rate and more precise compressor characteristics. [Copyright &y& Elsevier]

Abstract: Tradable emission permits have received considerable attention recently. One emerging issue is its interaction with electricity markets, specifically, market power in the permit markets. One challenge in studying market power in the permit markets is that the demand for permits is implicitly determined by the system conditions. Traditional ways of modeling market power (e.g., standard Cournot model) cannot directly apply. This paper develops a model in which Cournot firms in the market can manipulate the permit price through fringe firms. We have two central findings in this paper. First, diverting emission permits from Cournot to fringe producers always reduce the power and the permit prices, which could improve social surplus. Second, when the fringe producer’s emission rate is low, diverting permits from the “dirty” (i.e., more-polluting) Cournot producer to the “clean” (i.e., less-polluting) Cournot producer leads to a decline of both power and permit prices. Therefore, the initial allocation of the emission permits could be a useful regulatory tool to mitigate the abuse of market power. We also illustrate these results through a numerical simulation of the California electricity market. [Copyright &y& Elsevier]

Abstract: Comparing with conventional power plants, wind power generations are highly dependent on the natural environment. Especially under extreme wind condition, individual wind turbine might automatically disconnect from the grid once the wind speed is near or over the cut-out speed. This results in the output power reduction of a wind farm, which raises the security concern due to the imbalance between generation and demand, particularly for power systems with high wind power penetration. Different from the continuous wind power fluctuation under normal weather condition, the power output of individual wind turbine drops suddenly under extreme weather. Thus, the power reduction of individual wind turbine requires to be simulated by a higher resolution tool in order to illustrate the sudden power drop under an extreme condition. Also, the wind turbines are dispersed geographically within a wind farm and a region, which has a large impact on the magnitude of power reduction of a wind farm and the region under extreme weather. Therefore under an extreme weather condition, the simulation of wind power reduction requires a model with higher resolution as well as considering “geography dispersion”, which cannot be satisfied properly by most of current existing models. This paper hence proposes to use a model in frequency domain to assess the power reduction of wind farms under extremely high wind speed condition. Though this model is originally designed for application under normal wind condition, the originality of this paper is the study of the applicability of this model under an extreme wind condition, because this model provides a second-by-second simulation resolution and uses the coherence matrix in frequency domain to describe the coherences of power reduction among wind turbines. Also, for a regional wind farm cluster, an additional advantage of this model is to provide a reasonable estimate of wind power reduction under extreme wind condition without using extensive history data of the whole region. This model is verified by both qualitative and quantitative analysis and then some statistics-based tools are further developed to assess the reserve requirement due to wind power reduction under extreme wind condition. A case study of Zhangjiakou (ZJK) power region shows the effectiveness of the proposed assessment methodology in an extended wind power system region, and then this model is demonstrated to be valuable for both power system planning and operation with high wind penetration under extreme wind condition. [Copyright &y& Elsevier]

• A model for the study of CHP plants equipped with a battery is proposed. • A multi-objective approach is adopted to optimize size and number of the CHP plant. • The size of the battery energy storage systems is also optimized. • Optimally sized battery may enhance payback period and NPV results. • A reduction of the electrical power exchanged with the grid is also ensured. Combined heat and power (CHP) generation is a fundamental practice to reduce primary energy consumptions and mitigate the related greenhouse gas emissions. Cogeneration plants are even more important in the Hospital sector, as they give the opportunity to operate regardless of the electric power grid, so ensuring energy availability also during grid faults while providing significant cost savings to the public healthcare system. The integration of a battery energy storage with the CHP system could further increase the expected advantages by enabling more flexible operating strategies, further energy reliability and lower operating costs. Therefore, this research paper addresses the development of a specific methodology for the energetic and economic assessment of a grid connected CHP plant assisted by a battery energy storage. More specifically, an evaluation algorithm has been developed and then coupled to a genetic evolutionary algorithm. Then, a vector optimization problem has been solved to find the optimal configurations of a modular cogeneration plant (i.e. size and number of CHP gas engines; battery size) which maximize the primary energy saving while minimizing the payback period. Results based on electricity and heat demand of a hospital facility demonstrate how the battery energy storage system allows the shifting of the Pareto frontier towards better economic results if compared with Pareto solutions found when the optimization problem does not consider a battery storage supporting the CHP plant. The proposed methodology provides an effective and flexible procedure for the optimal configuration and detailed analysis of CHP plants integrated by an electrochemical based energy storage system. In fact, the effects of a high number of interacting energetic and economic parameters are considered together with the technical constraints required to extend battery lifetime in real applications. [ABSTRACT FROM AUTHOR]

Highlights • Rule-based control strategies suitably optimize hybrid electric car energy management. • Threshold-changing rule benefits hybrid vehicle charge sustainability and fuel usage. • Optimal primary source strategy outperforms all existing rule-based control methods. • Optimal primary source method operates vehicle similarly to optimization-based method. • Optimal primary source control is robust to drive conditions and real-time applicable. Abstract This paper proposes a new set of design principles to classify and design rule-based control strategies for the powertrain energy management of series hybrid electric vehicles. The design principles proposed consider the two most established rule-based control strategies for series hybrid electric vehicles, the Thermostat and the Power follower control strategies, and also an optimization-based control strategy, the Equivalent consumption minimization strategy, in terms of the mechanisms they employ to ensure charge sustaining operation and fuel efficient driving. Thus, the work then reflects upon the most effective design principles and derives a novel and superior rule-based control strategy for series hybrid electric vehicles that is claimed to outperform all the existing rule-based schemes in terms of fuel economy: the optimal primary source strategy (OPSS). The OPSS is implemented and then compared on a high fidelity hybrid electric vehicle model to Thermostat, Power follower and Equivalent consumption minimization strategies, as well as to a recently developed rule-based control strategy, the Exclusive operation strategy. As compared to conventional rule-based control strategies, the OPSS is found to deliver significantly improved fuel economy and which is remarkably close to that achieved by the optimization-based Equivalent consumption minimization strategy, while the design of the OPSS is simple and robust as compared to optimization-based strategies. The impressive performance is partly attributed to the recent improvements in engine start stop system technology. It is also shown that the battery is operated in a more steady manner, with a lower depth of discharge, consequently reducing battery degradation. [ABSTRACT FROM AUTHOR]

Abstract: With the generalized use of microelectronic devices, server computers and other susceptible equipment, the subject related to power quality (PQ) and its relationship to vulnerability of high performance plants are becoming an increasing concern to the industry. This paper addresses how uninterruptible power supply (UPS), particularly when configured in distributed DC mode, can become an energy efficient (EE) solution in high-tech buildings, especially when integrated with complimentary PQ measures. The paper is based on PQ audits conducted at different high-tech industries over the last years. It was found that the main problems for the equipment installed were voltage sags (or dips). Among all categories of electrical disturbances, voltage sags and momentary interruptions are the nemeses of the automated industrial process. The paper analyzes the capabilities of modern electronic power supplies and the convenience of embedded solution. Finally it is addresses the role of the Standards on the protection of electronic equipment and the implications for the final costumer. [Copyright &y& Elsevier]

Highlights • A combined heat, hydrogen and power (CHHP) system based on ammonia-SOFC is studied. • The CHHP system is sized to produce 100 kg/day of hydrogen for a refueling station. • The behavior of a single SOFC fed by ammonia is investigated by experimental tests. • The performance of the CHHP system is predicted by a thermo-electrochemical model. • The study is performed for two design concepts which differ for the SOFC unit size. Abstract In this paper a small-scale combined heat, hydrogen and power (CHHP) system, in which ammonia is used as a fuel for a solid oxide fuel cell (SOFC) to produce 100 kg/day of hydrogen (for refueling up to 20–30 fuel cell vehicles) as well as electricity for electric vehicles fast charging and heat for local use, is proposed. The study is carried out by means of experimental and numerical analysis; in particular, the behaviour of a single solid oxide cell fed by ammonia is investigated through experimental tests, while the design and the performance assessment of the proposed CHHP plant configurations are predicted by means of a thermo-electrochemical model, properly developed. The employed methodology and procedure are based on the SOFC power unit "sizing approach" that is performed through two different design concepts: (a) the minimum SOFC power unit size that allows to produce hydrogen and electricity for self-sustaining the electric power demand of the system; (b) the minimum SOFC power unit size that allows to produce hydrogen, to satisfy the system electric power demand and to supply electricity for EVs fast charging. Results in terms of performance (referred to the high heating values of input and output fuels) highlight that low values of electrical efficiency (14% for Concept_2) and thermal efficiency (≤15% for Concept_1 and Concept_2) are achieved, due to the high conversion of ammonia in hydrogen (the hydrogen production efficiencies are 70% and 42%, respectively), while the trigeneration efficiencies are high (81% and 71%). [ABSTRACT FROM AUTHOR]

Highlights • A systematic approach for the joint dispatch of energy and reserve is proposed. • A new demand response model is proposed to balance the flexibility and reliability. • Dynamic scenario generation and evaluation methods are proposed for wind profiles. • The inactive constraint reduction method is used to reduce the calculation burden. Abstract The intermittent nature of wind power increases the need for flexibility of the power system. This paper proposes the systematic approach for the joint dispatch of energy and reserve incorporating demand response, including the formulation of the two-stage optimization, dynamic scenario generation, and inactive constraint identification. The incentive-based demand response model is adopted to improve flexibility by its cooperation with conventional units. The dynamic scenario generation method is developed to provide reasonable input for the two-stage optimization, considering the temporal correlations of the wind power. Three indicators are proposed to evaluate the quality of scenarios. To speed up the solution, the inactive constraint reduction has been applied to reduce the computational burden raised by the number of the scenarios and the system scale. Finally, the modified IEEE 118-bus test system with fifty incentive-based demand response aggregators is utilized to evaluate the effectiveness of the proposed method to improve operational economics and to promote wind power utilization. Simulation results show that 89.53% of the transmission line constraints can be removed, leading to a maximal reduction of 69.81% of the computational time. Compared to the conventional sampling method, the dynamic scenario set performs better in terms of three proposed indicators, and can reduce the total cost by 1.99%. Neglecting the constraint of response times, the economic efficiency would be overestimated by 0.98%. [ABSTRACT FROM AUTHOR]

Highlights • Evaluation of uncoordinated electric vehicle charging in German transmission system. • Simulation of the congestion behaviour of the German transmission grid. • Development of a heuristic for redispatch capacity provision by electric vehicles. • Calculation of possible compensation for electric vehicle owners for participation. Abstract The increasing penetration of renewable generation in electricity markets as well as the rising number of electric vehicles pose new challenges for transmission grids. Additional demand and regionally clustered generation force system operators to consider costly expansion plans and employ expensive redispatch measures in the meantime. In this paper, we assess the ability of the expanded German transmission grid to cope with the additional demand of uncoordinated electric vehicle charging using a transportation problem formulation. We then propose local flexibility markets for electric vehicle owners to relieve the grid of congestion and to provide a heuristic that finds feasible solutions. We test our models on empirical data from the German electricity system of 2016. We find that the currently proposed expansion of the German electricity grid will not suffice to cope with increased electricity demand from uncoordinated electric vehicle charging. However, with coordination, electric vehicles can support transmission grid balancing and local flexibility markets can provide reasonable remuneration for electric vehicle owners. [ABSTRACT FROM AUTHOR]

Highlights • A day-ahead scheduling strategy is proposed with detailed device modeling. • The effects of multiple thermal storage devices on flexibility are fully utilized. • The unit commitment constraints in practical operation are well considered. • The optimal scheduling model is transformed into a MILP formulation by linearization. Abstract The operation of community integrated energy systems (CIESs) is challenging because it is necessary to couple energy production, conversion and consumption on the end-user side. Multiple energy demands need to be fed simultaneously with the goal of economy and reliability. This paper formulates the optimal scheduling problem based on a real community integrated energy system in China. The system has multiple chillers (i.e. three ground source heat pumps, two conventional water-cooled chillers and two double-duty chillers) and two types of thermal storage devices (i.e. two cold water tanks and an ice-storage tank). Based on detailed device modeling, a community integrated energy system operation strategy considering unit commitment is proposed, and then is transformed into a mixed-integer linear programming model by linearization of nonlinear items. Case studies are conducted based on the data for a typical day in summer. The results show that the proposed strategy can utilize the flexibility of the energy storage devices and realize an economic and reliable operation of the community integrated energy system by coordinating various energy devices. The strategy can also reduce the startup/shutdown frequency of chillers significantly. [ABSTRACT FROM AUTHOR]

Highlights • A review of the power quality standards to verify the applicability to DC microgrids. • An encompassing overview of the power quality issues in DC microgrids. • A simulation example to exemplify the less conceivable DC power quality issues. Abstract As compared to AC microgrids, DC microgrids reduce the hardware complexity of converter-dominated power distribution in the presence of a high number of renewable energy sources, energy storage systems and energy efficient loads. Another frequently highlighted advantage is their resiliency and tolerance against AC grid disturbances resulting in improved overall power quality. However, with respect to power quality, the question arises whether existing international power quality standards and metrics remain applicable in DC microgrids or require adjustments. Therefore, this paper critically revises the definitions and power quality indicators specified in IEC 61000 and IEEE Std1159. The resulting review is essential to unambiguously define the responsibilities of the microgrid operators, customers and device manufacturers. Apart from that, causes and consequences of power quality issues in DC microgrids are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Availability and reliability of electricity supplies have always been vexed issue in Nigeria. With an estimated population of 130 million people in AD 2005, Nigeria is the most populous country in Africa and belongs to the group of countries with the lowest electricity consumption per capita in the continent. Nigeria is also ranked among the poorest countries in the world. This paper examines the likely trend in the demand for electricity over the next 25 years under the assumptions that (i) there is a rapid economic development such that Nigeria transforms from low- to middle-income economy during this period, (ii) Nigeria meets the millennium development goals (MDG) in AD 2015, and (iii) the country achieves the status of an industrializing nation. For these to happen, this paper projects that electric-power generation will have to rise from the current capacity of 6500MW to over 160GW in AD 2030. This level of supply will be significant enough to increase the per capita electricity consumption to about 5000kWh per capita by the year 2030. Even then, this just compares with the AD 2003 per capital consumption of some industrializing countries. Analysis of the level of investment required to meet the projected power demand indicates that annual investment cost will rise from US$3.8 billion in AD 2006 to a peak of US$21 billion in AD 2028. The total investment stream over the 25 year period comes to US$262 billion or roughly US$10 billion per annum. [Copyright &y& Elsevier]

• We develop shallow and deep neural networks for the Short-Term Load Forecasting problem. • Different neural networks architectures are tested, including uni- and bi-directional structures. • Global climate models' information is used as input of the neural networks. • We present a real study case of time series forecasting for the Brazilian power system. • Relevant results are presented and systematically compared using Diebold-Mariano test. This paper focuses on the development of shallow and deep neural networks in the form of multi-layer perceptron, long-short term memory, and gated recurrent unit to model the short-term load forecasting problem. Different model architectures are tested, and global climate model information is used as input to generate more accurate forecasts. A real study case is presented for the Brazilian interconnected power system and the results generated are compared with the forecasts from the Brazilian Independent System Operator model. In general terms, results show that the bidirectional versions of long-short term memory and gated recurrent unit produce better and more reliable predictions than the other models. From the obtained results, the recurrent neural networks reach Nash-Sutcliffe values up to 0.98, and mean absolute percentile error values of 1.18%, superior than the results obtained by the Independent System Operator models (0.94 and 2.01% respectively). The better performance of the neural network models is confirmed under the Diebold-Mariano pairwise comparison test. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy, power system instability factors are rising. Transient voltage instability is a common power system problem that can cause blackouts and severe economic losses. An important measure to ensure transient voltage stability during emergencies is event-based load shedding (ELS). However, formulating ELS measures by experts gradually becomes inadaptable and time-consuming currently. With the increased complexity and uncertainty of modern new power systems, there is an urgent need for more intelligent and rapid ELS. This paper proposes a knowledge-enhanced parallel branching dueling Q-network (BDQ) framework for intelligent and rapid ELS against transient voltage instability. Firstly, an event-based Markov decision process (MDP) that differs from the conventional response-based MDP is established, which can effectively guide the training process. Secondly, to condense the huge conventional exponential decision space, a multi-branch BDQ structure is designed, which has higher training effectiveness and decision capability compared to branchless agents. Then, the domain proprietary knowledge that low-voltage buses are prioritized for ELS is incorporated into the BDQ agent. In comparison with a purely data-driven BDQ approach, incorporating knowledge can significantly enhance both training effectiveness and decision capability. Next, to further improve the applicability in large-scale real power systems, the parallel BDQ is proposed. Finally, the advantages of the proposed approach are demonstrated in the China Electric Power Research Institute 36-bus system and the Western Electricity Coordinating Council 179-bus system. [Display omitted] • Knowledge-enhanced parallel BDQ framework is proposed for transient voltage instability. • Multi-branch BDQ structure is designed to handle the huge exponential decision space. • Knowledge of prioritizing ELS on low-voltage buses can enhance decision capability. • Parallel BDQ training is proposed to improve application in actual large-scale power system. [ABSTRACT FROM AUTHOR]

Efficient and affordable electricity storage systems have a significant potential to support the growth and increasing penetration of intermittent renewable-energy generation into the grid from an energy system planning and management perspective, while differences in the demand and price of peak and off-peak electricity can make its storage of economic interest. Technical (e.g., roundtrip efficiency, energy and power capacity) as well as economic (e.g., capital, operating and maintenance costs) indicators are anticipated to have a significant combined impact on the competitiveness of any electricity storage technology or system under consideration and, ultimately, will crucially determine their uptake and implementation. In this paper, we present thermo-economic models of two recently proposed medium- to large-scale electricity storage systems, namely ‘Pumped-Thermal Electricity Storage’ (PTES) and ‘Liquid-Air Energy Storage’ (LAES), focusing on system efficiency and costs. The LAES thermodynamic model is validated against data from an operational pilot plant in the UK; no such equivalent PTES plant exists, although one is currently under construction. As common with most newly proposed technologies, the absence of cost data results to the economic analysis and comparison being a significant challenge. Therefore, a costing effort for the two electricity storage systems that includes multiple costing approaches based on the module costing technique is presented, with the overriding aim of conducting a preliminary economic feasibility assessment and comparison of the two systems. Based on the results, it appears that PTES has the potential to achieve higher roundtrip efficiencies, although this remains to be demonstrated. LAES performance is found to be significantly enhanced through the integration and utilisation of waste heat (and cold) streams. In terms of economics on the other hand, and at the system size intended for commercial application, LAES (12 MW, 50 MWh) is estimated in this work to have a lower capital cost and a lower levelised cost of storage than PTES (2 MW, 11.5 MWh), although it is noted that the prediction of the economic proposition of PTES technology is particularly uncertain if customised components are employed. However, when considering the required sell-to-buy price ratios, PTES appears (by a small margin) economically more competitive above an electricity buy price of ∼0.15 $/kWh, primarily due to its higher roundtrip efficiency. When considering the two systems at the same capacity, the costs are similar with a slight edge to PTES. Finally, it is of interest that the most expensive components in both systems are the compression and expansion devices, which suggests that there is a need to develop affordable high-performance devices for such systems. [ABSTRACT FROM AUTHOR]

Electric heat pumps (HPs), hybrid heating technology and district heating networks (DHN) are the main low-carbon heating technologies that can deliver the ambitious carbon reduction target in the UK. The optimal design of the heating system on a national scale to maximize the economic benefits regarding both investment cost and operation cost while satisfying the carbon target remains an open question. This paper aims to compare the economic advantages as well as the associated impacts on the electricity system under the full deployments of ASHP (air source HP), hybrid HP-Bs (ASHP and gas boilers) and DHNs. To do so, a novel whole-system approach is applied with the explicit consideration of the coordinated operation between heat and electricity system. The optimized strategy for heat sector decarbonisation is also demonstrated, providing an outline of the optimal deployment for different heating technologies in terms of their penetrations and deployed areas. The UK case study suggests the significant economic advantage of the hybrid HP-B over the other two heating technologies, while DHN may play an important role in urban areas under the optimized heat decarbonisation strategy. The results also clearly demonstrate the changes on the electricity side driven by the different decarbonisation strategies in the heating system. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine how a coordinated control strategy for managing the active power reserve in isolated microgrids is presented. This methodology can be applied in those microgrids where a specified generator assumes the role of the isochronous generator for the overall system. The derived algorithm evaluates control actions in the on-line environment by solving a constrained dynamic optimization problem aimed at maximizing the overall spinning reserve and, in particular, the reserve offered by the master unit equipped with the isochronous governor controller. The solution of this problem is obtained by adopting the direct Lyapunov theorem applied to the Sensitivity theory, ensuring the algorithm’s stability. Tests performed on the experimental microgrid, which has been built at the Polytechnic University of Bari, demonstrated the ability of the proposed methodology to share the regulation burden among all sources and storage systems, establishing adequate reserve margins of the master unit. [ABSTRACT FROM AUTHOR]

This study explores and quantifies the social costs and benefits of grid-scale electrical energy storage (EES) projects in Great Britain. The case study for this paper is the Smarter Network Storage project, a 6 MW/10 MWh lithium battery placed at the Leighton Buzzard Primary substation to meet growing local peak demand requirements. This study analyses both the locational and system-wide benefits to grid-scale EES, determines the realistic combination of those social benefits, and juxtaposes them against the social costs across the useful lifecycle of the battery to determine the techno-economic performance. Risk and uncertainty from the benefit streams, cost elements, battery lifespan, and discount rate are incorporated into a Monte Carlo simulation. Using this framework, society can be guided to cost-effectively invest in EES as a grid modernization asset to facilitate the transition to a reliable, affordable, and clean power system. [ABSTRACT FROM AUTHOR]

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance. [ABSTRACT FROM AUTHOR]