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The increasing demand of energy in the traditional grids is getting more complex, less feasible, harmful, uneconomical and high in power losses. This paper presents an efficient energy management approach to mitigate such issues with smart micro grid (SMG) and aims at a solution that is both cost effective and eco-friendly, within energy market paradigm. Goals are achieved with the help of Home Energy Management Controller (HEMC), Energy Market Management Controller (EMMC) and Control Agent (CA). The individual load is managed in the presence of local generation, storage system, user comfort, DGs and Utility within energy market paradigm. Two level energy management approach is proposed to achieve concerned goals. First is to manage load and schedule storage with respect to individual local generation and market pricing. Second is to manage energy market with the help of four different types of priorities and control agent input. The problem is solved with a variant of meta-heuristic method, Multi Objective Grey Wolf Optimization (MOGWO), which gives more comprehensive solution by comparing with Particle Swarm Optimization (PSO). The proposed methodology is implemented on a SMG based-community test system. Homes within that community have different economic conditions and personal priorities. Simulation results demonstrates achievement of aimed goals in presented work.

Ground-level ozone is a secondary pollutant produced by photochemical reactions and it adversely affects plant and human health. Taiyuan City, a typical city on the loess plateau, is suffering from severe ozone pollution. We utilized the data from eight national environmental monitoring sites of Taiyuan, including concentrations of O3 and nitric oxide, and meteorological factors, such as air temperature and wind, to study the pollution characteristics and sources of ozone (O3) in Taiyuan in 2018. Results show that during 2018, the maximum value and 90th percentile of the maximum 8-h running average of O3 concentration were 257 &mu, g/m&sup3, and 192 &mu, respectively. There were 72 days where the O3 concentration exceeded the standard in 2018, which were mainly during April to August. The O3 concentration increased from March, reached a high level in April through August, and decreased significantly from September. The O3 concentrations displayed a typical &ldquo, single peak&rdquo, diurnal variation, which was high during the day with peak at around 13:00-15:00 and low at night. From April to August, the O3 concentrations at Jinyuan was the highest, followed by Xiaodian and Taoyuan, and the O3 concentrations at Shanglan and Nanzhai were the lowest. When the O3 concentration exceeded the standard value, Jinyuan contributed the most to the O3 pollution of Taiyuan, followed by Taoyuan and Xiaodian. High temperature and pressure, south and southwest winds can lead to an increase in O3 concentration. The O3 pollution in the Taiyuan urban area is caused by local generation, and the transportation of polluted air masses containing oxides of nitrogen (NOx) and volatile organic compounds (VOCs) emitted by industries, such as the coking and steel plants in counties of Jinzhong City in southern Taiyuan, and Qingxu County, and some counties in Lyuliang City to the southwest. In addition, the mountain winds and low nitric oxide concentration are the main reasons for the increase of O3 concentration, often observed in Shanglan at night.

Ensuring access to reliable and sustainable power supply is becoming more and more challenging due to a combination of factors such as more frequent power grid outages caused by extreme weather events, the large-scale introduction of renewable energy resources that increases the complexity of the power system, but also aging infrastructure, supply and demand imbalance and power theft in some areas. Combined, all these factors can cause outages and together they can make electricity supply unreliable. The implications of this are many, ranging from minor inconveniences to major failures of critical infrastructures. A potential solution to ensure power supply during outages is to use local generation in the form of renewable resources to supply energy. This paper proposes a community-based mechanism that demonstrates that when community members can determine for themselves how excess energy generation is distributed, the power supply of specific members can be ensured. Self-determination is achieved by prioritizing and differentiating between community members as well as automatically and continuously redistributing energy, thereby adapting to sudden changes in supply and demand. Simulation results show that the proposed mechanism can be used to empower local communities to decide for themselves how local resources are distributed during events such as outages, ensuring prolonged power supply for differentiated members of affected communities. Harnessing the potential of renewable resources and smart technologies for intelligent coordination through empowerment of consumers to become pro-active participants is a promising solution for the future power systems.

In recent years, the distribution of local and renewable generation plants has introduced significant challenges in the management of electrical energy. In order to increase the usage of renewable energy, the prosumers, i.e., the residential users that can act both as producers and consumers, can benefit from joining together and forming energy communities. The deployment of an energy community is based both on technological advancements and on a deep understanding of human decision-making, which in turn requires knowledge about the factors that influence the behavior of residential users. This new scenario calls for great research investigations aimed to improve the management of energy exchanges inside energy communities. An important role in this context is played by the Internet of Things (IoT) technology, as smart IoT objects are used both as a source of real-time information regarding the energy production and the users&rsquo, requirements, and as actuators that can help to regulate the distribution and use of energy. In this paper, an IoT-aware optimization model for the energy management in energy communities is presented. The main novelty consists in modeling the entire energy community as a whole, rather than each prosumer separately, with the goal of optimizing the energy sharing and balance at the community level. Experimental results, performed in an university campus, show the advantages of the approach and its capability of reducing the energy costs and increasing the community&rsquo, s energy autonomy.

This study is an evaluation of the use and supply of compressed air, which is one of the most expensive energy sources in manufacturing, at Ford Motor Company's Livonia (Mich.) Transmission Plant. The aim of the study is to make recommendations to improve environmental and economic efficiency in future facilities. This paper presents a quantitative analysis of three compressed air supply patterns—plant air, point of use (POU), and local generation—as alternatives for future compressed air usage. Environmental Value systems (EnVS) tools are employed to analyze the economic and environmental performance of the three alternative supply patterns by using cost of ownership and environmental impact matrices. The results favor local generation over the other two alternatives in terms of economic and environmental considerations.