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Abstract Energy is a resource of strategic importance for cities, a fortiori global cities that rely on tremendous indirect energy embodied in interregional trades of service industries. The assessment of embodied energy in service industries is thus vital to the committed sustainable development of global cities and fundamental to tailor-made local policymaking. This paper applies Multi-Regional Input-Output analysis to the assessment of total embodied energy in service industries in six global cities, i.e., Beijing, Chongqing, Hong Kong, Shanghai, Singapore, and Tianjin. It is found that Hong Kong and Singapore have relatively lower energy use intensities compared with the four cities located in Mainland China. Service industries consume 17.02∼46.40 % of total embodied energy in the six cities. Coastal cities like Tianjin, Shanghai, Hong Kong, and Singapore have higher proportions of transportation consumption. The four Chinese mainland cities consume a larger proportion of coal than Hong Kong and Singapore. The method and the findings of this paper are expected to facilitate both the government and the industries in energy policymaking for a smarter and more sustainable urban economy.

Capítulos en libros This paper assesses the scalability and replicability of smart grid projects, by means of the identification of limitations and barriers faced by these projects. Factors have been identified that impact a project s scalability and replicability by means of a review of demonstration projects. The factors are of technical, economic, regulatory and social acceptance nature, and they describe requirements for scalability and replicability. Data was collected from ongoing European smart grid demonstration projects through the use of an elaborated questionnaire on the identified factors. Which allowed to quantify the status quo to what extent projects have taken these factors into account. Dew to their nature the derived factors can be categorized in technical, economic and regulatory & stakeholder related factor areas The obtained results allowed the identification of barriers, i.e. limitations regarding the scalability and replicability of smart grid projects. In particular projects face barriers regarding the absence of standards, the physical limits of the existing infrastructure, the absence of cost-benefit analyses, the neglect of varying macro-economic factors, stakeholder involvement and from the dependency of projects on regional or national regulatory frameworks and market designs. info:eu-repo/semantics/publishedVersion

The paper reports on the methodology and results of several studies on the long-term efficiency potentials in the two energy transformation steps from primary energy to final and useful energy and, more importantly, from useful energy to energy services. The examination of these potentials considers the lifetimes and periods of refurbishment of manufactured artefacts: Buildings and infrastructure that will save or waste energy in 2050 that are being built or restored today. The paper emphasises the enormous size of energy conservation potentials achievable not only by reducing energy losses but also by decreasing the specific activity levels for several energy services through improved material efficiency and intensified use of products, plants, and vehicles. It also reports on major definitive conclusions on R&D needs and challenges to R&D-policies as well as immediate policy action in OECD-countries in order to realise the vision of reducing primary energy use per capita by two thirds in industrialised countries within the next five decades.

With an increasing number of controllable distributed energy resources deployed and integrated into the power system, how to economically manage these distributed resources will become a challenge for the future smart grid. To solve the issue, consensus based distributed economic dispatch algorithms have been introduced in the literature as computationally scalable approaches. However, in real-world applications with imperfect communications networks, the performance of consensus-based economic dispatch algorithms degrades when information losses occur. In this paper, a robust distributed system incremental cost estimation (RICE) algorithm is introduced to solve the Economic Dispatch Problem (EDP) in a smart grid environment in a distributed way considering communications information losses. Unlike the existing consensus-based algorithms to solve EDP, RICE algorithm has two updating layers running in parallel in each distributed controller: one layer uses the gossip updating rule to estimate the system's average power mismatch, while the other layer uses the consensus updating rule to update the system Incremental Cost (IC) estimation. In this approach, the vulnerability of consensus-based algorithms to communications information losses is eliminated. The convergence and optimality of the algorithm are guaranteed as long as the undirected communications topology among local controllers is connected. Several case studies are presented to illustrate the performance of the proposed algorithm, and show the robustness under different information loss scenarios with different communications topologies. Display Omitted The classic Economic Dispatch Problem is solved in a distributed manner.The proposed RICE algorithm is robust to communication information losses.The proposed RICE algorithm is highly scalable.Rigorous analysis is provided to prove the convergency and optimality.

AbstractThe present study was aimed to develop a membrane sparger (MS) integrated into a tubular photobioreactor to promote the increase of the carbon dioxide (CO2) fixation by Spirulina sp. LEB 18 cultures. The use of MS for the CO2 supply in Spirulina cultures resulted not only in the increase of DIC concentrations but also in the highest accumulated DIC concentration in the liquid medium (127.4 mg L−1 d−1). The highest values of biomass concentration (1.98 g L−1), biomass productivity (131.8 mg L−1 d−1), carbon in biomass (47.9% w w−1), CO2 fixation rate (231.6 mg L−1 d−1), and CO2 use efficiency (80.5% w w−1) by Spirulina were verified with MS, compared to the culture with conventional sparger for CO2 supply. Spirulina biomass in both culture conditions had high protein contents varying from 64.9 to 69% (w w−1). MS can be considered an innovative system for the supply of carbon for the microalgae cultivation and biomass production. Moreover, the use of membrane system might contribute to increased process efficiency with a reduced cost of biomass production.

Residential, commercial, and industrial buildings have been reported to consume a large portion of the generated energy. With the introduction of smart grid and its energy optimization techniques, it is now possible to efficiently manage and control consumers’ energy usage to fulfil their demands with the existing energy generation infrastructure, which otherwise seems to be a backbreaking challenge. This paper presents an efficient energy management solution for buildings with a large number of thermostatic devices (air conditioners) that maintain the temperature of different thermal zones in a predefined range. The primary objective of this paper is to schedule the thermostatic devices in order to reduce total energy consumption by these devices when they are in operation for a very long duration of time, while maintaining the other constraints. We formulate it as a graph problem where minimum mean cycle will provide the desired solution. The proposed methodology ensures that at no point in time the power consumption goes beyond a certain peak power consumption limit. We also enhance the methodology to reduce peak load consumption. Furthermore, a fast greedy approach has been developed to efficiently scale up the aforementioned scheduling scheme for a large number of devices. Experimental results show that significant improvements can be obtained by the proposed approaches over existing algorithms in reducing average energy consumption.

Geoenergy sources will continue to be mainstays of the world’s energy mix for many years to come, but the technological and business realities behind these energy sources are changing in two fundamental ways. First, with much of the world’s “easy oil” already consumed, the companies behind geoenergy will have to use increasingly sophisticated technologies to find and deliver these energy sources to the market. Second, the expectations placed upon the geoenergy sector by many of its stakeholders have grown considerably with regards to environmental stewardship, safety, and human welfare. In the face of these kinds of challenges, the industry will require an increasing degree of technological and commercial sophistication to continue to be a part of the world’s sustainable energy mix. Blockchain has emerged as a promising innovation that could potentially play an important role in delivering the kinds of technological and commercial capabilities that the geoenergy sector will need to achieve these ends. In spite of the myriad ways that blockchain could potentially improve the efficiency and sustainability of the geoenergy industry, however, the technology is still evolving, and a few barriers stand in the way of its widespread deployment. This paper puts forward case study evidence from the Intel Corporation and the Energistics Consortium showing what the geoenergy sector can learn about blockchain from other industries, and highlights that the absence of data standards and interoperability has contributed to blockchain’s failure to deliver significant value in the geoenergy domain thus far.

Abstract This paper proposes a stochastic multi-objective unit commitment (SMOUC) problem incorporating smart grid technologies (SGTs), namely, plug-in electric vehicles (PEVs), demand response programs (DRPs), compressed air energy storage (CAES) units, and renewable distributed generations (DGs). An economic emission analysis of the proposed SMOUC problem with the SGTs is carried out to minimize the total expected operation cost and emission using a new mixed-integer linear programming (MILP) method. A two-stage stochastic programming method is used for dealing with the uncertain nature of power generation from the renewable DGs. Lexicographic optimization in combination with hybrid augmented-weighted e -constraint method are employed to obtain Pareto optimal solutions of the SMOUC problem, and a fuzzy decision making is applied to select the most preferred non-dominated solution. Besides, mathematical modeling of responsive loads can help the independent system operator (ISO) to use a conservative and reliable model to have lower error in load curve characteristic estimation, such as variation in peak load. In this regard, this paper also contributes to the existing body of knowledge by developing linear and nonlinear economic models of price responsive loads for time-based DRPs (TBDRPs), as well as voluntary and mandatory incentive-based DRPs (IBDRPs) based on the customer’s behavior (CB) concept and price ratio (PR) parameter. Also, new mathematical indices are proposed to choose the most conservative and reliable economic model of price responsive loads. Moreover, different widely used DRPs are analyzed and prioritized using the strategy success index (SSI) from the ISO viewpoint to determine the most effective DRP which has more coordination with the SGTs. The proposed MILP-based SMOUC problem with integrated SGTs, is applied to IEEE 10-unit test system and is implemented in General Algebraic Modeling System (GAMS) environment. Simulation analyses demonstrate the effectiveness of integrating SGTs into the proposed SMOUC problem from the economic, environmental, and technical points of view.

This paper focuses on the planning of electricity resources in the developing electricity infrastructure. First we model the existing infrastructure and extend this model to a smart grid infrastructure, where we focus on the large scale introduction of small electricity generators, leading to generation possibilities at both ends of the electricity network. Then the traditional Unit Commitment Problem (UCP) is given. We extend this formulation to the Multilevel Unit Commitment Problem (MUCP), where we describe and include the possibilities that arise in the developing smart grid, in a general way. Based on the characteristics of the problem with its subdivision into different levels, a planning method for the MUCP is described. Finally we solve and analyze a scenario, where a fleet of 5000 houses is added to a small collection of power plants.