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A key challenge for future energy systems is how to minimize the effects of employing demand response (DR) programs on the consumer. There exists a diverse range of consumers with a variety of types of loads, such as must-run loads, and this can reduce the impact of consumer participation in DR programs. Multi-energy systems (MES) can solve this issue and have the capability to reduce any discomfort faced by all types of consumers who are willing to participate in the DRPs. In this paper, the most recent implementations of DR frameworks in the MESs are comprehensively reviewed. The DR modelling approach in such energy systems is investigated and the main contributions of each of these works are included. Notably, the amount of research in MES has rapidly increased in recent years. The majority of the reviewed works consider power, heat and gas systems within the MES. Over three-quarters of the papers investigated consider some form of energy storage system, which shows how important having efficient, cost-effective and reliable energy storage systems will be in the future. In addition, a vast majority of the works also considered some form of demand response programs in their model. This points to the need to make participating in the energy market easier for consumers, as well as the importance of good communication between generators, system operators, and consumers. Moreover, the emerging topics within the area of MES are investigated using a bibliometric analysis to provide insight to other researchers in this area.

The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and discharged based on the energy availability within the smart grid communities and real time energy pricing. The benefits of optimal power-sharing techniques with the RESs and BBSS are analyzed by taking three different scenarios which are simulated by C++ software package. Extensive case studies are carried out to validate the effectiveness of the proposed energy management scheme. It is demonstrated that the proposed method can save energy and reduce electricity cost up to 35% and 45% compared to the existing methods.

The aim of this paper is to present the state of development of renewable energy sources (RES) in Poland in accordance with the obligations of European Union energy policy. The EU Member States are obliged to adopt different support mechanisms for the development of renewable energy sources, and in consequence to achieve their Directive’s targets. Poland, being a Member State of the EU since 2004, has accepted a target of a 15% share of energy generated from renewable energy sources in final energy consumption, including 19.3% from renewable electricity until 2020. Due to the difficulties with target achievement, the authors found it reasonable to analyze the challenge of RES development in Poland. The article presents energy policy in the EU, as well as the review of measures implemented for renewable energy development. The current state of and perspectives on using renewable energy sources in Poland and in the EU are also depicted. In the article, the authors analyze the relation between reference prices at dedicated RES auctions in Poland and the levelized cost of electricity (LCOE). The paper also provides a description of the renewable energy sources’ development in three areas: electricity, heat and transport biofuels. Its main content, though, refers to the generation of electricity from renewable energy sources.

This paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a scheduling subsystem that optimizes the utility of energy storage assets, and an active/pro-active control subsystem. The active control strategy provides secondary DR services, through optimizing a multi-objective cost function formulated using a weight-based routing algorithm. In this context, the total weight of each edge between any two consecutive nodes is calculated as a function of thermal comfort, cost (tariff), and the rate at which electricity is consumed over a short future time horizon. The pro-active control strategy provides primary DR services. Furthermore, tertiary DR services can be processed to initiate a sequence of operations that enables the continuity of applied electrical services for the duration of the demand side event. Computer simulations and a case study using hardware-in-the-loop testing is used to evaluate the optimization and control module. The main conclusion drawn from this research shows the real-time operation of the proposed optimization and control scheme, operating on a prototype platform, underpinned by the effectiveness of the new methods and approach for tackling the optimization problem. This research recommends deployment of the optimization and control scheme, at scale, for decentralized community energy management. The paper concludes with a short discussion of business aspects and outlines areas for future work.

In the grid-tied micro-grid context, energy resilience can be defined as the time period that a local energy system can supply the critical loads during an unplanned upstream grid outage. While the role of renewable-based micro-grids in enhancing communities’ energy resilience is well-appreciated, the academic literature on the techno-economic optimisation of community-scale micro-grids lacks a quantitative decision support analysis concerning the inclusion of a minimum resilience constraint in the optimisation process. Utilising a specifically-developed, time-based resilience capacity characterisation method to quantify the sustainability of micro-grids in the face of different levels of extended grid power outages, this paper facilitates stakeholder decision-making on the trade-off between the whole-life cost of a community micro-grid system and its degree of resilience. Furthermore, this paper focuses on energy infrastructure expansion planning, aiming to analyse the importance of micro-grid reinforcement to meet new sources of electricity demand—particularly, transport electrification—in addition to the business-as-usual demand growth. Using quantitative case study evidence from the Totarabank Subdivision in New Zealand, the paper concludes that at the current feed-in-tariff rate (NZ$0.08/kWh), the life cycle profitability of resilience-oriented community micro-grid capacity reinforcement is guaranteed within a New Zealand context, though constrained by capital requirements.

Cogeneration is becoming increasingly popular in building and community energy systems with demands on electricity and heat, which is suitable for residential and industrial use in remote areas. This paper considers a stand-alone cogeneration energy hub. The electrical and thermal energies are produced by a combined heat and power (CHP) unit, photovoltaic panels, and a solar thermal collector. Since solar units generate no electricity and heat during the night, energy storage units which shift demands over time can promote the usage of solar energy and reduce the fuel cost of the CHP unit. This paper proposes a method to retrieve the optimal operation cost as an explicit function in the capacity parameters of electric and thermal energy storage units, reflecting the value of energy storage in the cogeneration energy hub. The capacity parameter set is divided into a collection of polyhedrons, on each polyhedron, the optimal value is an affine function in the capacity parameters. Furthermore, the optimal sizes of system components are discussed. The capacity of the CHP unit is determined from a linear program, ensuring supply adequacy, the capacities of solar generation and energy storage units are calculated based on the cost reduction and the budget. Case studies demonstrate the effectiveness of the proposed method.

Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France.

On the issues of global environment protection, the renewable energy systems have been widely considered. The photovoltaic (PV) system converts solar power into electricity and significantly reduces the consumption of fossil fuels from environment pollution. Besides introducing new materials for the solar cells to improve the energy conversion efficiency, the maximum power point tracking (MPPT) algorithms have been developed to ensure the efficient operation of PV systems at the maximum power point (MPP) under various weather conditions. The integration of reinforcement learning and deep learning, named deep reinforcement learning (DRL), is proposed in this paper as a future tool to deal with the optimization control problems. Following the success of deep reinforcement learning (DRL) in several fields, the deep Q network (DQN) and deep deterministic policy gradient (DDPG) are proposed to harvest the MPP in PV systems, especially under a partial shading condition (PSC). Different from the reinforcement learning (RL)-based method, which is only operated with discrete state and action spaces, the methods adopted in this paper are used to deal with continuous state spaces. In this study,DQN solves the problem with discrete action spaces, while DDPG handles the continuous action spaces. The proposed methods are simulated in MATLAB/Simulink for feasibility analysis. Further tests under various input conditions with comparisons to the classical Perturb and observe (P&amp, O) MPPT method are carried out for validation. Based on the simulation results in this study, the performance of the proposed methods is outstanding and efficient, showing its potential for further applications.

The cyber–physical system (CPS) architecture provides a novel framework for analyzing and expanding research and innovation results that are essential in managing, controlling and operating complex, large scale, industrial systems under a holistic insight. Power systems constitute such characteristically large industrial structures. The main challenge in deploying a power system as a CPS lies on how to combine and incorporate multi-disciplinary, core, and advanced technologies into the specific for this case, social, environmental, economic and engineering aspects. In order to substantially contribute towards this target, in this paper, a specific CPS scheme that clearly describes how a dedicated cyber layer is deployed to manage and interact with comprehensive multiple physical layers, like those found in a large-scale modern power system architecture, is proposed. In particular, the measurement, communication, computation, control mechanisms, and tools installed at different hierarchical frames that are required to consider and modulate the social/environmental necessities, as well as the electricity market management, the regulation of the electric grid, and the power injection/absorption of the controlled main devices and distributed energy resources, are all incorporated in a common CPS framework. Furthermore, a methodology for investigating and analyzing the dynamics of different levels of the CPS architecture (including physical devices, electricity and communication networks to market, and environmental and social mechanisms) is provided together with the necessary modelling tools and assumptions made in order to close the loop between the physical and the cyber layers. An example of a real-world industrial micro-grid that describes the main aspects of the proposed CPS-based design for modern electricity grids is also presented at the end of the paper to further explain and visualize the proposed framework.

To meet the rapid-growing demand for electricity in Kuwait, utility planners need to be informed on the energy consumption to implement energy efficiency measures to manage sustainable load growth and avoid the high costs of increasing generation capacities. The first step of forecasting the future energy profile is to establish a baseline for Kuwait (i.e., a business-as-usual reference scenario where no energy efficiency incentives were given and the adoption of energy efficient equipment is purely market-driven). This paper presents an investigation of creating a baseline end-use energy profile until 2040 for the residential sector in Kuwait by using a bottom-up approach. The forecast consists of mainly two steps: (1) Forecasting the quantity of the residential energy-consuming equipment in the entire sector until 2040 where this paper used a stock-and-flow model that accounted for the income level, electrification, and urbanization rate to predict the quantify of the equipment over the years until 2040, and (2) calculate the unit energy consumption (UEC) for all equipment types using a variety of methods including EnergyPlus simulation models for cooling equipment. By combining the unit energy consumption and quantity of the equipment over the years, this paper established a baseline energy use profile for different end-use equipment for Kuwait until 2040. The results showed that the air conditioning loads accounted for 67% of residential electrical consumption and 72% of residential peak demand in Kuwait. The highest energy consuming appliances were refrigerators and freezers. Additionally, the air conditioning loads are expected to rise in the future, with an average annual growth rate of 2.9%, whereas the lighting and water heating loads are expected to rise at a much lower rate.

Within the United States solar energy industry, there is a general motto of “set it and forget it” with solar energy. This notion is derived from much of the research and reliability studies around the photovoltaic (PV) panels themselves, not necessarily the PV system as a whole (including the inverter and other components). This implies that maintenance and regular monitoring is not needed. Yet many things can go wrong to cause the actual performance to deviate from the expected performance. If failures and/or unanticipated degradation issues go undetected, they will lead to reduced energy generation (and associated electricity credits) and/or potential loss of component warranty because of manufacturer turnover. Given the size of the problem and gaps with current solutions, the authors propose that PV system owners need an unbiased third-party off-the-shelf system-level predictive maintenance tool to optimize return-on-investment and minimize time to warranty claim in PV installations. This paper reviews the literature highlighting challenges, current approaches, and opportunities for PV predictive maintenance. The paper concludes with a call to action for establishing a collaborative agenda toward prioritizing PV predictive maintenance.

High-temperature fuel cells are one of the devices currently investigated for an integration into distributed power supply grids. Such distributed grids aim at the simultaneous production of thermal energy and electricity. To maximize the efficiency of fuel cell systems, it is reasonable to track the point of maximum electric power production and to operate the system in close vicinity to this point. However, variations of gas mass flows, especially the concentration of hydrogen contained in the anode gas, as well as variations of the internal temperature distribution in the fuel cell stack module lead to the fact that the maximum power point changes in dependence of the aforementioned phenomena. Therefore, this paper first proposes a real-time capable stochastic filter approach for the local identification of the electric power characteristic of the fuel cell. Second, based on this estimate, a maximum power point tracking procedure is derived. It is based on an iteration procedure under consideration of the estimation accuracy of the stochastic filter and adjusts the fuel cell&rsquo, s electric current so that optimal operating points are guaranteed. Numerical simulations, based on real measured data gathered at a test rig available at the Chair of Mechatronics at the University of Rostock, Germany, conclude this paper.

The Smart Grid is one of the most important solutions to boost electricity sharing from renewable energy sources. Its implementation adds new functionalities to power systems, which increases the electric grid complexity. To ensure grid stability and security, systems need flexible methods in order to be tested in a safe and economical way. A promising test technique is Power Hardware-In-the-Loop (PHIL), which combines the flexibility of Hardware-In-the-Loop (HIL) technique with power exchange. However, the acquisition of PHIL components usually represents a great expense for laboratories and, therefore, the setting up of the experiment involves making hard decisions. This paper provides a complete guideline and useful new tools for laboratories in order to set PHIL facilities up efficiently. First, a PHIL system selection guide is presented, which describes the selection process steps and the main system characteristics needed to perform a PHIL test. Furthermore, a classification proposal containing the desirable information to be obtained from a PHIL test paper for reproducibility purposes is given. Finally, this classification was used to develop a PHIL test online database, which was analysed, and the main gathered information with some use cases and conclusions are shown.

The paper investigates an innovative ironless linear generator, installable inside a wave energy converter, in order to produce electricity from sea waves. This energy source is considered strategic for the future, especially in small islands, however, this technology is still far from the commercial phase. Considering the wave energy potential of the Mediterranean Sea, a first prototype of the electrical linear generator was realized at the Department of Engineering of Palermo University. This machine can be run by a two-floating buoys system, able to produce a linear vertical motion. The main goal of this paper is the investigation of the advantages and the disadvantages of the utilization of steel materials to realize the stator of linear generators. Thus, starting from the prototype, the authors analyzed the effects produced by the replacement of steel in the stator with a non-magnetic material. For comparison, the authors evaluated the amplitude of no-load voltages, using a three-phase connection scheme, and the amplitude of the magnetic force produced by the interaction of magnets with the stator. Both aspects were evaluated through numerical simulations and mathematical models.

Demand Response (DR) aims to motivate end consumers to change their energy consumption patterns in response to changes in electricity prices or when the reliability of the electrical power system (EPS) is compromised. Most of the proposals found in the literature only aim at reducing the cost for end consumers. However, this article proposes a home energy management system (HEMS) that aims to schedule the use of each home appliance based on the price of electricity in real-time (RTP) and on the consumer satisfaction/comfort level in order to guarantee the stability and the safety of the EPS. Thus, this paper presents a multi-objective DR optimization model which was formulated as a multi-objective nonlinear programming problem subjected to a set of constraints and was solved using the Non-Dominated Sorted Genetic Algorithm (NSGA-II), in order to determine the scheduling of home appliances for the time horizon. The multi-objective DR optimization model not only to minimize the cost of electricity consumption but also to reduce the level of inconvenience for residential consumers. Moreover, a priori, it is expected to obtain a more uniform demand with fewer peaks in the system and, potentially, achieving a more reliable and safer EPS operation. Thus, the energy management controller (EMC) within the HEMS determines an optimized schedule for each home appliance through the multi-objective DR model presented in this article, and ensures a more economic scenario for end consumers. In this paper, a performance evaluation of HEMS in 15 Brazilian families between 1 January and 31 December 2016 is presented with different electric energy consumption patterns in the cities of Bel&eacute, m&mdash, PA, Teresina&mdash, PI, Cuiab&aacute, &mdash, MT, Florian&oacute, polis&mdash, SC and S&atilde, o Paulo&mdash, SP, with three families per city, located in the regions north, northeast, central west, south and the southeast of Brazil, respectively. In addition, a total of 425 home appliances were used in the simulations. The results show that the HEMS achieved reductions in the cost of electricity for all the Scenarios used while minimally affecting the satisfaction/comfort of the end consumers as well as taking into account all the restrictions. The largest reduction in the total cost of electricity occurred for the couple without children, resident in the city of Teresina&mdash, PI, with a drop from US$ 99.31 to US$ 90.72 totaling 8.65% savings in the electricity bill. Therefore, the results confirm that the proposed HEMS effectively improves the operating efficiency of home appliances and reduces electricity costs for end consumers.

This paper presents a visualization methodology, in the form of a multi-dimensional techno-economic assessment diagram, to comprehensively illustrate the relationship between assumptions (sets of input parameters) and results (corresponding output variables). This methodology is applied to analyze the lifecycle costs and CO2 emissions of hybrid vehicles (HVs) and electric vehicles (EVs). This paper then develops an eight-dimensional interactive diagram showing the relative advantages of HVs or EVs in the input space consisting of the following parameters: HV fuel efficiency, EV energy efficiency, total mileage travelled gasoline price, electricity price, battery price, gasoline CO2 intensity, and electricity CO2 intensity. This methodology provides a map illustrating the comprehensive relationship between the inputs and outputs in the model used, where specific scenarios (specific sets of inputs and their outputs) are represented by points plotted on the map. This methodology can be used in systematic comparisons of electric vehicles and related uncertainty analyses.

This paper describes a system to support the development and assessment of methodologies that explore the energy flexibility provided by electric water heaters. The proposed system follows a modular approach and allows users to share and remotely control the referred devices. The operation of this system is presented in this paper considering a case study where the energy flexibility provided by a 100 L electric water heater is used to reduce electricity costs under the Portuguese tariff context. The collected results reveal that increasing the available energy flexibility leads to larger savings. These results also show that the referred savings depend on the instant associated to hot water consumption events.

Electric Vehicles (EVs) are increasing the interdependence of transportation policies and the electricity market dimension. In this paper, an Electricity Market Model with Electric Vehicles (EMMEV) was developed, exploiting an agent-based model that analyzes how carbon reduction policy in transportation may increase the number of Electric Vehicles and how that would influence electricity price. Agents are Energy Service Providers (ESCOs) which can distribute fuels and their objective is to maximize their profit. In this paper, the EMMEV is used to analyze the impacts of the Low-Carbon Fuel Standard (LCFS), a performance-based policy instrument, on electricity prices and EV sales volume. The agents in EMMEV are regulated parties in LCFS should meet a certain Carbon Intensity (CI) target for their distributed fuel. In case they cannot meet the target, they should buy credits to compensate for their shortfall and if they exceed it, they can sell their excess. The results, considering the assumptions and limitations of the model, show that the banking strategy of the agents contributing in the LCFS might have negative impact on penetration of EVs, unless there is a regular Credit Clearance to trade credits. It is also shown that the electricity price, as a result of implementing the LCFS and increasing number of EVs, has increased between 2% and 3% depending on banking strategy.

In this paper, we address the problem of minimizing the total daily energy cost in a smart residential building composed of multiple smart homes with the aim of reducing the cost of energy bills and the greenhouse gas emissions under different system constraints and user preferences. As the household appliances contribute significantly to the energy consumption of the smart houses, it is possible to decrease electricity cost in buildings by scheduling the operation of domestic appliances. In this paper, we propose an optimization model for jointly minimizing electricity costs and CO2 emissions by considering consumer preferences in smart buildings that are equipped with distributed energy resources (DERs). Both controllable and uncontrollable tasks and DER operations are scheduled according to the real-time price of electricity and a peak demand charge to reduce the peak demand on the grid. We formulate the daily energy consumption scheduling problem in multiple smart homes from economic and environmental perspectives and exploit a mixed integer linear programming technique to solve it. We validated the proposed approach through extensive experimental analysis. The results of the experiment show that the proposed approach can decrease both CO2 emissions and the daily energy cost.

The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase optimization decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users’ historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN) model based on “meteorological similarity day (MSD)” to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm- back propagation (GA-BP) neural network model based on fuzzy clustering (FC) to predict the short-term market clearing price (MCP). Thirdly, based on Sealed-bid Auction (SA) in game theory, a Bayesian Game Model (BGM) of the power retailer’s bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE) under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the decision-making optimization of the sales company.

The electricity market of China is currently in the process of a new institutional reform. Diversified electricity retail entities are gradually being established with the opening of the marketing electricity side. In the face of a complex market environment and fierce competition, the operating efficiency can directly reflect the current market position and development of electricity retail companies. TOPSIS method can make full use of the information of original data, calculate the distance between evaluated objects and the ideal solutions and get the relative proximity, which is generally used in the overall department and comprehensive evaluation of the benefits. Least squares support vector machine (LSSVM), with high convergence precision, helps save the training time of algorithm by solving linear equations and is used to predict the comprehensive evaluation value. Considering the ultimate goal of sustainable development, a comprehensive evaluation model on operating efficiency of electricity retail companies based on the improved TOPSIS method and LSSVM optimized by modified ant colony algorithm is proposed in this paper. Firstly, from the view of sustainable development, an operating efficiency evaluation indicator system is constructed. Secondly, the entropy weight method is applied to empower the indicators objectively. After that, based on the improved TOPSIS method, the reverse problem in the evaluation process is eliminated. According to the relative proximity between the evaluated objects and the absolute ideal solutions, the scores of comprehensive evaluation for operating efficiency can then be ranked. Finally, the LSSVM optimized by modified ant colony algorithm is introduced to realize the simplified expert scoring process and fast calculation in the comprehensive evaluation process, and its improved learning and generalization ability can be used in the comprehensive evaluation of similar projects. The example analysis proves that the comprehensive evaluation model proposed in this paper can provide scientific and effective evaluation results of the operating efficiency of electricity retail companies.

Growing home comfort is causing increasing energy consumption in residential buildings and a consequent stress in urban medium and low voltage distribution networks. Therefore, distribution system operators are obliged to manage problems related to the reliability of the electricity system and, above all, they must consider investments for enhancing the electrical infrastructure. The purpose of this paper is to assess how the reduction of building electricity consumption and the modification of the building load profile, due to load automation, combined with suitable load control programs, can improve network reliability and distribution efficiency. This paper proposes an extensive study on this issue, considering various operating scenarios with four load control programs with different purposes, the presence/absence of local generation connected to the buildings and different external thermal conditions. The study also highlights how different climatic conditions can influence the effects of the load control logics.

One of the most important objectives of the European Union is the creation of an Internal Electricity Market (IEM) in Europe. Various national electricity market designs represent one of the major obstacles to the creation of IEM and it is necessary to coordinate national market designs among Member States in order to achieve that. Therefore, this paper gives a comprehensive overview of the electricity market design according to laws related to the electricity sector that are in force in Croatia. Additionally, the paper identifies key obstacles that hamper the development of a well-functioning electricity market in Croatia towards its further integration into the IEM. Since Croatian Power Exchange (CROPEX) is not coupled with any other power exchange, special attention was given to necessary prerequisites for market coupling with the Slovenian Power Exchange (BSP SouthPool) and with the Hungarian Power Exchange (HUPX).

Australia is an energy net self-sufficient country rich in energy resources, from fossil-based to renewable energy. Australia, a huge continent with low population density, has witnessed impressive reduction in energy consumption in various sectors of activity in recent years. Currently, coal and natural gas are two of Australia's major export earners, yet its abundant renewable energy resources such as solar, wind, and tidal, are still underutilized. The majority of Asian countries, on the other hand, are in the middle of economic expansion, with increasing energy consumption and lack of energy resources or lack of energy exploration capability becoming a serious challenge. Electricity interconnection linking two or more independent grids within a country or at cross-border or regional levels has found its way into electricity markets worldwide. This concept allows for electricity exchanges that lead to optimized use and sharing of electricity generated from different sources. The interconnection also enables the long distance exploitation of renewable energy which would otherwise be physically impossible. ASEAN (Association of Southeast Asian Nations) and other regional groupings in Asia have initiated a number of interconnections to gain economic benefits. Asian's hunger for energy for its economic development, climate change that has become a global and urgent issue to be solved, and Australia's abundant renewable energy resources have all prompted increasing interest in a super-grid interconnection linking Australia to Asian grids, the Australian-Asian (Power) Grid (AAG). This paper overviews the existing grid interconnections as well as current initiatives at domestic, sub-regional, and regional levels worldwide, with a particular focus on Asia. The paper concludes with a critical appraisal on the benefits, potential, challenges and issues to be encountered by the AAG initiative. Refereed/Peer-reviewed

Wave power is an abundant source of energy that can be utilized to produce electricity. Therefore, assessments of wave power resources are being carried out worldwide. An overview of the recent assessments is presented in this paper, revealing the global distribution of these resources. Additionally, a study, which aims to assess the spatial distribution of the Baltic Sea near-shore wave power potential along the coast of Klaipeda (Lithuania), is introduced in this paper. The impacts of the wave propagation direction and decreasing depth on wave power resources were examined using the numerical wind-wave model MIKE 21 NSW. The wave height loss of the design waves propagating to shore was modelled, and the wave power fluxes in the studied depths were calculated using the JONSWAP wave spectrum modified for the Baltic Sea. The results revealed that all waves that propagate to the shore in the Baltic Sea near-shore area along the coast of Klaipeda from 30 m depth to 5 m depth lose at least 30% of their power. Still, most common waves in this area are low, and therefore, they start to lose their power while propagating to the shore at relatively low (10-14 m) depths. To turn this into an advantage the wave power converter would have to work efficiently under low power conditions. Keywords: near-shore wave power; spatial distribution; propagation direction impact; depth impact; Baltic Sea.

The integration of wind power into power grid will bring some impacts on the multiple subjects of electric power system. Economic impacts of wind power integration on multiple subjects of China’s electric power system were quantitatively assessed from Quasi-public goods property perspective in this paper. Firstly, the Quasi-public goods property of transmission services provided by power grid corporations was elaborated. Secondly, the multiple subjects of China’s electric power system, which include electricity generation enterprises (EGEs), power grid corporations (PGCs), electricity consumers (ECs), and environment, were detailed analyzed. Thirdly, based on the OPF-based nodal price model and transmission service cost allocation model, the economic impact assessment model of wind power integration was built from Quasi-public goods property perspective. Then, the IEEE-24 bus system employed in this paper was introduced according to current status of China’s electric power system, and the modeling of wind turbine was also introduced. Finally, the simulation analysis was performed, and the economic impacts of wind power integration on EGEs, PGCs, ECs and Environment were calculated. The results indicate, from Quasi-public goods property perspective, the wind power integration will bring positive impacts on EGEs, PGCs and Environment, while negative impacts on ECs. The findings can provide references for power system managers, energy planners, and policy makers.

Carbon footprints have been widely employed as an indicator for total carbon dioxide released by human activities. In this paper, we implemented a multi-regional input-output framework to evaluate the carbon footprints and embodied carbon flows for the eight regions of China from consumption-based perspective. It is found that the construction, electricity/stream supply, and machine manufacturing rank as the top sectors with the largest total carbon emissions. The construction sector alone accounts for 20%–50% of the national emissions. Besides the sectoral carbon footprints, regional footprints and their differences in carbon emissions were also observed. The middle region had the largest total carbon footprints, 1188 million ton, while the capital region ranked the first for its per capita carbon footprint, 7.77 ton/person. In regard to the embodied carbon flows within China, the study detected that the embodied carbon flows take up about 41% of the total carbon footprints of the nation. The northwest region and the eastern coast region are found to be the largest net embodied carbon exporter and importer, respectively. Further investigation revealed significant differences between production-based and consumption-based carbon emissions, both at sectoral and total amounts. Results of this paper can provide specific information to policies on sectoral and regional carbon emission reduction.

A linear function submission-based double auction (LFS-DA) mechanism for a regional electricity network is proposed in this paper. Each agent in the network is equipped with a battery and a generator. Each agent simultaneously becomes a producer and consumer of electricity, i.e., a prosumer, and trades electricity in the regional market at a variable price. In the LFS-DA, each agent uses linear demand and supply functions when they submit bids and asks to an auctioneer in the regional market. The LFS-DA can achieve an exact balance between electricity demand and supply for each time slot throughout the learning phase and was shown capable of solving the primal problem of maximizing the social welfare of the network without any central price setter, e.g., a utility or a large electricity company, in contrast with conventional real-time pricing (RTP). This paper presents a clarification of the relationship between the RTP algorithm derived on the basis of a dual decomposition framework and LFS-DA. Specifically, we proved that the changes in the price profile of the LFS-DA mechanism are equal to those achieved by the RTP mechanism derived from the dual decomposition framework, except for a constant factor.

This paper proposes a polygeneration system based on a multi-input chemical looping combustion system, which generates methanol and electricity, through the use of natural gas and coal. In this system, the chemical looping hydrogen (CLH) production system and the coal-based methanol production system are integrated. A high quality fuel, natural gas, is used to improve the conversion ratio of coal. The Gibbs energy of the two kinds of fuels is fully used. Benefitting from the chemical looping process, 27% CO2 can be captured without energy penalty. With the same outputs of methanol and electricity, the energy savings ratio of the new system is about 12%. Based on the exergy analyses, it is disclosed that the integration of synthetic utilization of natural gas and coal plays a significant role in reducing the exergy destruction of the new system. The promising results obtained in this paper may lead to a clean coal technology that will utilize natural gas and coal more efficiently and economically.

China has now become the largest country in carbon emissions all over the world. Furthermore, with transportation accounting for an increasing proportion of CO2 emissions year by year, the transportation sector has turned out to be one of the main sectors which possesses a high growth speed in CO2 emissions. To accurately analyze potentially influencing factors which accelerate the process of CO2 emissions of transportation sector in China, based on carbon accounting by the checklists method of Intergovernmental Panel on Climate Change’s (IPCC), in this paper, we propose a decomposition model using Logarithmic Mean Divisia Index (LMDI) decomposition analysis technology and modified fixed growth rate method. Then effects of six influencing factors including energy structure, energy efficiency, transport form, transportation development, economic development and population size from 2001 to 2014 were quantitatively analyzed. Consequently, the results indicate that: (1) economic development accounts most for driving CO2 emissions growth of the transportation sector, while energy efficiency accounts most for suppressing CO2 emissions growth; (2) the pulling effects of natural gas, electricity and other clean energy consumption on CO2 emissions growth offset the inhibitory effects of traditional fossil fuels, making energy structure play a significant role in promoting CO2 emissions growth; (3) the inhibitory effects of railways and highways lead to inhibitory effects of transport form on CO2 emissions growth; (4) transportation development plays an obvious role in promoting CO2 emissions, while the effects of population size is relatively weaker compared with those of transportation development. Furthermore, the decomposition model of CO2 emissions factors in transport industry constructed in this paper can also be applied to other countries so as to provide guidance and reference for CO2 emissions analysis of transportation industry.

Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

The paper combines original concepts about eco-energetic systems, in a transdisciplinary sustainable context. Firstly, it introduces the concept of M.E.N. (Mega-Eco-Nega-Watt), the eco-energetic paradigm based on three different but complementary ecological economic spaces: the Megawatt as needed energy, the Ecowatt as ecological energy, and the Negawatt as preserved energy. The paper also deals with the renewable energies and technologies in the context of electrical energy production. Secondly, in the context of the M.E.N. eco-energetic paradigm, comprehensive definitions are given about eco-energetic systems and for pollution. Thirdly, the paper introduces a new formula for the eco-energetic efficiency which correlates the energetic efficiency of the system and the necessary newly defined ecological coefficient. The proposed formula for eco-energetic efficiency enables an interesting form of relating to different situations in which the input energy, output energy, lost energy, and externalities involved in an energetic process, interact to produce energy in a specific energetic system, in connection with the circular resilient economy model. Finally, the paper presents an original energetic diagram to explain different channels to produce electricity in a resilience regime, with high eco-energetic efficiency from primary external energetic sources (gravitation and solar sources), fuels (classical and radioactive), internal energetic sources (geothermal, volcanoes) and other kind of sources. Regardless the kind of energetic sources used to obtain electricity, the entire process should be sustainable in what concerns the transdisciplinary integration of the different representative spheres as energy, socio-economy, and ecology (environment).

Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre- processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI) is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX) electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.

The maritime industry, among all other industries, is being forced to gradually reduce its emissions. Legislation is one of the tools applying this pressure, and from 1 January 2020, it focuses on the reduction of sulfur percentage in the heavy fuel oil (HFO)-powered vessels to 0.5%. In the beginning of this paper, the harmful environmental contribution of the naval sector is presented, along with the current legislation. The maritime industry is in a transitional stage, diverging from fossil fuels through alternative technologies and fuels, aiming to become over the long term a zero-emission industry. However, there are many implemented technologies, mostly of a mechanical nature, that already improve the efficiency of vessels and indirectly reduce their emissions. Such technologies include shaft generators (SGs), scrubbers, etc. The aim is for alternative fuels and technologies such as solar and wind to be implemented, too. Such technologies, when combined with the advantages of digitalization and automation, can further reduce emissions toward zero-emission vessels (ZEVs) through integrated systems. The present paper serves the purpose of a common point of gathering, addressing, and explaining the latest updates, previous achievements, and future targets of the maritime sector. The very nature of the subject—electric propulsion in the maritime sector—makes it very difficult to find sufficient and trustworthy data. There are two main reasons for this problem. The first one is that electric vehicles became commercial at a large scale (electric cars) very recently, and are still in a transitional stage. The second reason is that the maritime industry is very competitive; therefore, state-of-the-art technologies and data that give each company the lead are rarely published, and when they do, it happens very discreetly. In the quantitative part of the paper, where the photovoltaic (PV) and battery system calculations take place, there is no use of a specific model rather than a simplified approach. The purpose of the calculations is to show that with the present technologies, a purely solar-powered commercial vessel (such as RoRo, passenger, etc.) is technically impossible, and that there could be only a small contribution—of around 7%—to the electricity needs of a roll-on/roll-off (RoRo)-passenger ship. The state of the art finds a very short number of vessels that already use battery propulsion, but is expected to increase in the upcoming years. The present paper not only presents an overview of the state-of-the-art achievements in the electric propulsion of vessels, it also considers the exploitation of the continuous growth that the battery market is facing. As stated before, batteries are on the up, and this is due to the emerging need for energy storage in electricity grids that depend increasingly on renewable energy sources (RES). The paper makes a first consideration about the feasibility and possible benefits of implementing grid-like battery systems on-board vessels. In such a scenario, vessels would acquire significantly bigger energy capacity, allowing greater travel distances, a possible contribution of 44% of the vessel’s total power requirements (propulsion included), and a surplus as far as electricity requirements are concerned. There is also the more futuristic long-term scenario where Green Ports would charge vessels purely from RES dedicated to the port’s needs. The last part of the paper contains a qualitative assessment about the possible impacts that a battery-powered maritime industry could have.

This paper investigates the economic impact of an energy efficiency improvement policy on electricity-intensive firms in Ghana. The policy imposed a penalty on these electricity-intensive firms, which are referred to as special load tariff (SLT) customers, when their power factor was below 90%. This paper applies the regression discontinuity design (RDD) to the panel data of these SLTs ranging from 1994 to 2012, excluding those years characterized by energy crisis. The results show adverse impacts of the policy on the employment and salary levels of the firms in the long run, in particular, the small- and medium–voltage firms. The results indicate that small- and medium–voltage firms are economically vulnerable to the penalty policy in the long run and recommend two policies to overcome this challenge. Firstly, the penalty for power factor improvement should not be imposed identically across firms with different voltage levels. Secondly, firms that satisfy the power factor standard should receive subsidies to improve their competitiveness in the market.

The new round of electricity market reform in 2015 completely changed the profit pattern of power grid enterprises (PGEs) in China, and directly affected their investment plans. Under the new electricity market reform (NEMR), the government regulatory authority made higher requirements for the investment efficiency of PGEs, and the investment effectiveness hence became the core criterion for investment plans. Therefore, the PGEs are now attaching great importance to the investment efficiency. According to their geographical differences, this paper divides the Chinese provincial PGEs into three groups, namely eastern, central and western region enterprises. Based on the NEMR, we developed an evaluation system of investment efficiency for the above-mentioned enterprises. Moreover, this paper selects GDP per capita, electricity consumption in industry, and electrification rate as external environment variables, and conducts an empirical research on the investment efficiency of 31 provincial PGEs in China in 2017. The analysis reveals that three external environment variables have considerable impacts on the investment efficiency. Though the increase of GDP per capita and electricity consumption in industry are not conducive to improving investment efficiency, the advancement of electrification plays a positive role in its improvement. And from the real efficiency results, Tianjin, Liaoning, Jiangsu, and Fujian have relatively higher investment efficiency, while Henan, Shandong, and Shanghai exhibit lower investment efficiency. By comparing the investment efficiency of PGEs in the first and third stage, conclusions can be drawn that in the first stage the investment efficiency of PGEs was overestimated, and the inefficient investments prevailed some provincial PGEs, which caused by low scale efficiency.

Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.

Recent movements for the decarbonization of the electricity sector have become a priority for many countries around the world and will inevitably lead to the sharp decline of fossil-fuel-based energy. Energy from fossil fuels is to be replaced by renewable energy sources (RES), although the transition will neither be cheap nor smooth. One sustainable and environmentally friendly alternative to fossil fuels and which will take a considerable share in the increasing supply of renewable energy resources is biofuels. There are various types of biofuels used in practice, however, biodiesels represent one of the most popular and widespread ones. This paper focuses as a case study on the byproducts of Jatropha curcas, a crop and a plant that is already used for biofuel production and which is subsequently employed in electricity generation in Jatropha curcas producing regions. This paper identifies the limitations and prospects of Jatropha curcas utilization. Also, Jatropha curcas is compared to other materials suitable for biomass generation. An economic analysis for a 2 MW biofuel powerplant was conducted incorporating various market-related risks. The study shows that at current prices, net profitability can be achieved using Jatropha curcas byproducts for producing electricity.

The demand for electricity has been rising significantly over the past years and it is expected to rise further in the coming years due to economic and societal development. Smart grid technology is being developed in order to meet the rising electricity requirement. In order for the smart grid to perform its full functions, the Energy Management Systems (EMSs), especially Home Energy Management Systems (HEMS) are essential. It is necessary to understand the energy demand of the loads and the energy supply either from the national grid or from renewable energy technologies. To facilitate the Demand Side Management (DSM), Heat Pumps (HP) and air conditioning systems are often utilised for heating and cooling in residential houses due to their high-efficiency power output and low CO2 emissions. This paper presents a program for a HEMS using a Particle Swarm Optimisation (PSO) algorithm. A HP is used as the load and the aim of the optimisation program is to minimise the operational cost, i.e., the cost of electricity, while maintaining end-user comfort levels. This paper also details an indoor thermal model for temperature update in the heat pump control program. Real measured data from the UK Government&rsquo, s Renewable Heat Premium Payment (RHPP) scheme was utilised to generate characteristic curves and equations that can represent the data. This paper compares different PSO variants with standard PSO and the unscheduled case calculated from the data for five winter days in 2019. Among all chosen algorithms, the Crossover Subswarm PSO (CSPSO) achieved an average saving of 25.61% compared with the cost calculated from the measured data with a short search time of 1576 ms for each subswarm. It is clear from this work that there is significant scope to reduce the cost of operating a HP while maintaining end user comfort levels.

This paper explores the alternative roles hydrogen can play in the future European Union (EU) energy system, within the transition towards a carbon-neutral EU economy by 2050, following the latest policy developments after the COP21 agreement in Paris in 2015. Hydrogen could serve as an end-use fuel, a feedstock to produce carbon-neutral hydrocarbons and a carrier of chemical storage of electricity. We apply a model-based energy system analysis to assess the advantages and drawbacks of these three roles of hydrogen in a decarbonized energy system. To this end, the paper quantifies projections of the energy system using an enhanced version of the PRIMES energy system model, up to 2050, to explore the best elements of each role under various assumptions about deployment and maturity of hydrogen-related technologies. Hydrogen is an enabler of sectoral integration of supply and demand of energy, and hence an important pillar in the carbon-neutral energy system. The results show that the energy system has benefits both in terms of CO2 emission reductions and total system costs if hydrogen technology reaches high technology readiness levels and economies of scale. Reaching maturity requires a significant investment, which depends on the positive anticipation of market development. The choice of policy options facilitating visibility by investors is the focus of the modelling in this paper.

Considering that the European Directive has imposed that at least 20% of the total energy should come from renewable energy sources (RES) by 2020 already and the specific targets for each European Union Member State, this paper attempts to assess the importance of GDP per capita in realizing these targets and also the effects of the RES share in electricity. Contrary to previous research, this paper does not consider the connection between economic growth and RES, but rather the potential connection between the share of RES in electricity and the real GDP per capita. The panel data models indicated to a positive, but very low impact of GDP per capita on the share of RES in electricity in the period of 2007-2017 in the case of the EU countries, except Luxembourg that has outlier values of GDP per capita. However, causality between the two variables was not identified. Some groups of countries were described according to these variables using cluster analysis. Future research should focus on the extension of this model by including other important variables such as RES potential available in the countries with specific geographical conditions. Keywords: renewable energy sources; electricity; GDP per capita.

Over the past eight years, off-grid systems, in the form of stand-alone solar home systems (SHSs), have proved the most popular and immediate solution for increasing energy access in rural areas across the Global South. Although deployed in significant numbers, issues remain with the cost, reliability, utilization, sustainability and scalability of these off-grid systems to provide higher-tiered energy access. Interconnection of existing stand-alone solar home systems (SHSs) can form a microgrid of interconnected prosumers (i.e., households owning SHS capable of producing and consuming power) and consumers (i.e., households without an SHS, and only capable of consuming power). This paper focuses on the role of a smart energy management (SEM) platform in the interconnection of off-grid systems and making bottom-up electrification scalable, and how it can improve the overall sustainability, efficiency and flexibility of off-grid technology. An interconnected SHS microgrid has the potential to unlock latent generation and storage capacity, and so effectively promote connected customers to higher tiers of energy access. This approach can therefore extend the range of products currently used by people located in the remote areas of developing countries to include higher-power devices such as refrigerators, TVs and potentially, electric cookers. This paper shows the results of field studies in the Northern Province of Rwanda within off-grid villages where people mainly rely on SHSs as a source of electricity. These field studies have informed further simulation-based studies that define the principal requirements for the operation of a smart energy management platform for the interconnection of SHSs to form a community microgrid.

Since 2013, a series of air pollution prevention and control (APPC) measures have been promulgated in China for reducing the level of air pollution, which can affect regional short-term electricity power demand by changing the behavior of power users electricity consumption. This paper analyzes the policy system of the APPC measures and its impact on regional short-term electricity demand, and determines the regional short-term load impact factors considering the impact of APPC measures. On this basis, this paper proposes a similar day selection method based on the best and worst method and grey relational analysis (BWM-GRA) in order to construct the training sample set, which considers the difference in the influence degree of characteristic indicators on daily power load. Further, a short-term load forecasting method based on least squares support vector machine (LSSVM) optimized by salp swarm algorithm (SSA) is developed. By forecasting the load of a city affected by air pollution in Northern China, and comparing the results with several selected models, it reveals that the impact of APPC measures on regional short-term load is significant. Moreover, by considering the influence of APPC measures and avoiding the subjectivity of model parameter settings, the proposed load forecasting model can improve the accuracy of, and provide an effective tool for short-term load forecasting. Finally, some limitations of this paper are discussed.

Technological advancement, environmental concerns, and social factors have made plug-in electric vehicles (PEVs) popular and attractive vehicles. Such a trend has caused major impacts to electrical distribution systems in terms of efficiency, stability, and reliability. Moreover, excessive power loss, severe voltage deviation, transformer overload, and system blackouts will happen if PEV charging activities are not coordinated well. This paper presents an optimal charging coordination method for a random arrival of PEVs in a residential distribution network with minimum power loss and voltage deviation. The method also incorporates capacitor switching and on-load tap changer adjustment for further improvement of the voltage profile. The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper. The proposed method considers a time-of-use (ToU) electricity tariff such that PEV users will get more benefits. The random PEV arrival is considered based on the driving pattern of four different regions. To demonstrate the effectiveness of the proposed method, comprehensive analysis is conducted using a modified of IEEE 31 bus system with three different PEV penetrations. The results indicate a promising outcome in terms of cost and the distribution system stress minimization.

This paper presents an assessment of the cost performance of CO2 capture technologies when retrofitted to a cement plant: MEA-based absorption, oxyfuel, chilled ammonia-based absorption (Chilled Ammonia Process), membrane-assisted CO2 liquefaction, and calcium looping. While the technical basis for this study is presented in Part 1 of this paper series, this work presents a comprehensive techno-economic analysis of these CO2 capture technologies based on a capital and operating costs evaluation for retrofit in a cement plant. The cost of the cement plant product, clinker, is shown to increase with 49 to 92% compared to the cost of clinker without capture. The cost of CO2 avoided is between 42 &euro, /tCO2 (for the oxyfuel-based capture process) and 84 &euro, /tCO2 (for the membrane-based assisted liquefaction capture process), while the reference MEA-based absorption capture technology has a cost of 80 &euro, /tCO2. Notably, the cost figures depend strongly on factors such as steam source, electricity mix, electricity price, fuel price and plant-specific characteristics. Hence, this confirms the conclusion of the technical evaluation in Part 1 that for final selection of CO2 capture technology at a specific plant, a plant-specific techno-economic evaluation should be performed, also considering more practical considerations.

This paper analyses the implementation of renewable off-grid technologies in rural areas, especially where an extension to the national electricity grid was not considered economically feasible. Implementation of remote, stand-alone, electricity technologies as alternatives to a grid connection to provide sustainable electricity access have often failed with many planned projects not realised or abandoned. Our initial assumption was that stand-alone electricity project exhibiting higher scores on sustainability indicators would benefit communities more and make their endurance more likely. However, the impact of the stand-alone technology was often overruled or its quality weakened by government preferences wishing to realise a connection to the central electricity grid. Empirically, the study compares three cases of stand-alone micro-hydropower projects and three cases of stand-alone solar photovoltaic projects in Bogor Regency, Indonesia. It is based on qualitative document analysis, complemented by multiple rounds of semi-structured interviews and observations. The paper assesses the extent to which each project met indicators of technical, economic, social, environmental, and institutional sustainability. The paper tries to explain the endurance of the project from these sustainability scores and uses additional explanations from Ostrom&rsquo, s Institutional Analysis and Development (IAD) framework. The findings show that, for the studied local communities, the attractiveness of a grid connection overrules the virtues of a stand-alone electricity project, despite its quality, successful operation and impact. Our research also shows that government policy priorities changed in the rural electrification programme for some communities. In these situations, the off-grid rural electrification programme predominantly provided only temporary access to sustainable electricity for remote local communities that remained waiting and hoping for a grid connection to connect them to fossil fuel-dominated electricity.

With the economic and social development of China, the continuous growth of the energy demand is the trend for now and the future. As a consequence, distributed energy, especially distributed electricity power generation, has received more and more attention. Thus, the scale and utilization level of distributed energy has been continuously improved. However, due to the limitations of current technologies, resources, policies and other issues, the comprehensive benefits and synergy levels of energy sources need to be greatly enhanced. Based on the system dynamics model, this paper examines the factors affecting the comprehensive benefits of distributed energy in China, screens the key subjects, and using the literature review method, combined with the existing literature analysis, constructs a comprehensive benefit evaluation index system and evaluates the comprehensive benefits through case analysis. This paper also sorts out the distributed energy-related Chinese government policies from 2001 to 2017, and considers the scale of distributed energy development, then divides it into two development stages. The synergetic entropy is used to analyze the synergetic development degree of the two-stage distributed energy entities. The synergistic optimization strategy is proposed from the Chinese government side, power supply side, power grid side and user side, which provides theoretical methods and optimization suggestions for improving the comprehensive benefits of distributed energy and promoting sustainable development of energy.