The electricity and transportation industries are the main sources of greenhouse gas emissions on Earth. Renewable energy, mainly wind and solar, can reduce emission from the electricity industry ...(mainly from power plants). Likewise, next-generation plug-in vehicles, which include plug-in hybrid electric vehicles (EVs) and EVs with vehicle-to-grid capability, referred to as "gridable vehicles" (GVs) by the authors, can reduce emission from the transportation industry. GVs can be used as loads, energy sources (small portable power plants), and energy storages in a smart grid integrated with renewable energy sources (RESs). Smart grid operation to reduce both cost and emission simultaneously is a very complex task considering smart charging and discharging of GVs in a distributed energy source and load environment. If a large number of GVs is connected to the electric grid randomly, peak load will be very high. The use of traditional thermal power plants will be economically and environmentally expensive to support the electrified transportation. The intelligent scheduling and control of GVs as loads and/or sources have great potential for evolving a sustainable integrated electricity and transportation infrastructure. Cost and emission reductions in a smart grid by maximum utilization of GVs and RESs are presented in this paper. Possible models for GV applications, including the smart grid model, are given, and results are presented. The smart grid model offers the best potential for maximum utilization of RESs to reduce cost and emission from the electricity industry.
Energy systems transformation Dangerman, A T C Jérôme; Schellnhuber, Hans Joachim
Proceedings of the National Academy of Sciences - PNAS,
02/2013, Letnik:
110, Številka:
7
Journal Article
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The contemporary industrial metabolism is not sustainable. Critical problems arise at both the input and the output side of the complex: Although affordable fossil fuels and mineral resources are ...declining, the waste products of the current production and consumption schemes (especially CO ₂ emissions, particulate air pollution, and radioactive residua) cause increasing environmental and social costs. Most challenges are associated with the incumbent energy economy that is unlikely to subsist. However, the crucial question is whether a swift transition to its sustainable alternative, based on renewable sources, can be achieved. The answer requires a deep analysis of the structural conditions responsible for the rigidity of the fossil-nuclear energy system. We argue that the resilience of the fossil-nuclear energy system results mainly from a dynamic lock-in pattern known in operations research as the “Success to the Successful” mode. The present way of generating, distributing, and consuming energy—the largest business on Earth—expands through a combination of factors such as the longevity of pertinent infrastructure, the information technology revolution, the growth of the global population, and even the recent financial crises: Renewable-energy industries evidently suffer more than the conventional-energy industries under recession conditions. Our study tries to elucidate the archetypical traits of the lock-in pattern and to assess the respective importance of the factors involved. In particular, we identify modern corporate law as a crucial system element that thus far has been largely ignored. Our analysis indicates that the rigidity of the existing energy economy would be reduced considerably by the assignment of unlimited liabilities to the shareholders.
In this paper, optimal cooperative operational planning of distributed energy resources (DER) in an active distribution network (ADN) with soft open points (SOP) has been investigated. To do so, a ...new two-layer coordinated optimization framework ADN has been proposed considering SOP. In the upper layer, the location and sizing of the DERs and SOPs has been optimised. While inner layer executes simultaneous optimal functioning of volt/var control (VVC) devices, remote controlled switches (RCS), DERs, and SOPs. The main objective of proposed methodology is to reduce total investment cost of DERs and SOPs as well as operating costs of the system over the planning horizon. Besides, objective function considers the cost of energy not served (ENS), the cost of substation-purchased electricity, and the cost of carbon emissions. Meanwhile, a stochastic module has been employed to address high-level uncertainties associated to renewables and load demands. Besides, the effects of distribution network reconfiguration (DNR) and conservation voltage reduction (CVR) have been taken into account. For various instances, the suggested framework was implemented on an IEEE 119-bus distribution system and solved using a proposed hybrid optimization solver. The efficacy of proposed DER with SOP methodology has been validated on various cases and also tested on sudden external disturbances such as under voltage and over voltage problem conditions. When compared to standard planning schemes, the test results show that it is beneficial in boosting system efficiency, increasing reliability, and lowering the carbon footprint of distribution systems.
•DERs are distributed generation, storage,electric vehicles, and demandresponses.•DERs aremodelledusingbothprobabilistic and deterministic methods.•DER optimisation mainly focusses on sizingand ...location toimprove the network.•DER integration commonly involves analysis on the IEEE test feeder.•DERs improve theresilience of the distribution network.
Distributed energy resources (DERs) have gained particular attention in the last few years owing to their rapid deployment in power capacity installation and expansion into distribution systems. DERs mainly involve distributed generation and energy storage systems; however, some definitions also include electric vehicles, demand response strategies, and power electronic devices used for their coupling with power grids. DERs challenge the entire operating system owing to their heterogeneous energy generation from renewable energy sources, the probabilistic nature of electric vehicle charging, and end-user exponential integration of power electronic devices. Research on DER integration has been conducted in the academic and industrial sectors. This study proposes a schematic literature review of DERs, including its modelling, description of deterministic and probabilistic power flow methods, power grid topologies for studies, and impacts of DERs on power grid operation. DERs are primarily modelled using probabilistic approaches. The most frequently optimized DER variables are sizing and location. Meanwhile, the most critical variables to analyse during their integration process to the power grid are voltage profile, frequency response, and charging of both lines and transformers, followed by less-proportional power quality indicators. Overall, DERs can improve the resilience of energy systems because they provide voltage and frequency support, reduce energy losses, enhance power quality indicators, and enhance energy recovery in extreme scenarios such as high-impact low-probability events.
A high proportion of nonrenewable energy in total energy consumption is leading the world economies toward severe environmental issues. This has motivated the environmentalists to come forward for ...encouraging masses around the world to use environmentally friendly renewable energy instead of environment-damaging nonrenewable energy. The UNDP is also playing its role in spreading the importance of clean energy in goal 7 of the SDGs. Therefore, the present study is aimed to inquire about the asymmetric influence of clean and unclean energy consumption on environmental quality in environmentally poor economies. In the present study, data from forty-two environmentally poor economies for the period from 1995 to 2019 are extracted, and a heterogeneous pooled mean group estimator is employed to estimate the results. The findings expose that the use of clean energy initially improves transport-based environmental quality while it damages industry-based environmental quality in the beginning. In the later stage, the use of clean energy hurts transport-based environmental quality while it improves industry-based environmental quality. This concludes that the use of clean energy reveals a U-shaped impact on transport-based environmental quality but it has an inverted U-shaped impact on industry-based environmental quality in the selected economies. The results further expose that use of unclean energy is leaving an inverted U-shaped influence on transport-based environmental quality while it leaves a U-shaped impact on industry-based environmental quality. These results are important from a policy perspective as these help in controlling the net carbon emissions by targeting transport-based environmental quality in environmentally poor economies.
•The study finds asymmetric impact of energy on environmental quality.•The PMG estimator on an annual data series from 1995 to 2019 is applied.•Clean energy discloses U-shaped impact on transport based environmental quality.•It exposes inverted U-shaped impact on industrial based environmental quality.•Unclean energy reports opposite results as compared to the results of clean energy.
Natural gas from tight shale formations will provide the United States with a major source of energy over the next several decades. Estimates of gas production from these formations have mainly ...relied on formulas designed for wells with a different geometry. We consider the simplest model of gas production consistent with the basic physics and geometry of the extraction process. In principle, solutions of the model depend upon many parameters, but in practice and within a given gas field, all but two can be fixed at typical values, leading to a nonlinear diffusion problem we solve exactly with a scaling curve. The scaling curve production rate declines as 1 over the square root of time early on, and it later declines exponentially. This simple model provides a surprisingly accurate description of gas extraction from 8,294 wells in the United States’ oldest shale play, the Barnett Shale. There is good agreement with the scaling theory for 2,057 horizontal wells in which production started to decline exponentially in less than 10 y. The remaining 6,237 horizontal wells in our analysis are too young for us to predict when exponential decline will set in, but the model can nevertheless be used to establish lower and upper bounds on well lifetime. Finally, we obtain upper and lower bounds on the gas that will be produced by the wells in our sample, individually and in total. The estimated ultimate recovery from our sample of 8,294 wells is between 10 and 20 trillion standard cubic feet.
The reduction of global greenhouse gas emissions is one of the key steps towards sustainable development. The integration of Distributed Energy Resources (DERs) in power systems will help with ...emissions reduction. Virtual Power Plants (VPPs) can overcome barriers to participation of DERs in system operation. In this paper, a model is proposed for the energy management of a VPP including PhotoVoltaic (PV) modules, wind turbines, Electrical Energy Storage (EES) systems, Combined Heat and Power (CHP) units, and heat-only units. The multi-objective operational scheduling of DERs in the VPP focuses on maximizing the expected day-ahead profit of the VPP and minimizing the expected day-ahead emissions. The uncertainty of wind speed, solar radiation, market price, and electrical load is modeled using scenario based approach. Also, two-stage stochastic programming is implemented for modeling the VPP energy management. Three cases have been investigated for evaluating the proposed method: single-objective scheduling of VPP to maximize profit, single-objective scheduling of VPP to minimize emission and multi-objective economic/emission scheduling of VPP. The results indicate the appropriate economic and environmental performance of the proposed method, which provides the possibility of selecting a compromise solution for the VPP operator in accordance with environmental restrictions and economic constraints.
•Multi-objective scheduling of a VPP to maximize profit and minimize emissions.•Considering various DERs including wind turbine, PV, CHP, EES and heat-only unit.•Using heuristic algorithms to solve non-linear and non-convex problem of scheduling.•Modeling the uncertainties of wind speed, solar radiation, market price, and load.•Using two-stage stochastic programming for modeling the optimization problem.
Abstract A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may ...be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.
The ever-increasing energy demand and high penetration rate of distributed renewable generation brings new challenges to the planning of power distribution networks. This paper proposes an expansion ...planning model for distribution networks by considering multiple types of energy resources in distribution side, including shared electric vehicle (SEV) charging stations, solar-based distributed generation sources, and battery energy storage systems. The operational characteristics of SEV are considered and modeled. The proposed planning model aims to minimize the weighted sum of network investment cost, energy losses, and queue waiting time of SEVs. A stochastic scenario generation method is introduced to address the stochastic feature of SEVs' driving behaviors. Numerical studies are tested on the systems with 54-node distribution network and 25-node traffic network.
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