A Hybrid Energy Sharing Framework for Green Cellular Networks

SUMMARYIn the future, renewable energy (RE) generation systems will be constructed and interconnected to the power system by nonutility entities such as generation companies and residential customers. Therefore, generation expansion planning (GEP) should consider the effects of RE generation. The purpose of this paper is to discuss the best GEP from the economic, supply reliability, and environmental aspects considering the penetration of RE generation. For this purpose, this paper proposes a model of a utility's GEP process considering RE generation. The proposed method is based on stochastic dynamic programming (SDP) in which long‐term uncertainties are modeled by geometric Brownian motion (GBM) and the binomial lattice process. The variation of RE generation output due to the weather conditions (short‐term uncertainty) is also considered in the proposed method by means of the net load curve.

Berkeley Lab’s annual status report on U.S. renewables portfolio standards (RPS) provides an overview of key trends associated with U.S. state RPS policies. The report, published in slide-deck form, describes recent legislative revisions, key policy design features, compliance with interim targets, past and projected impacts on renewables development, and compliance costs. The 2017 edition of the report presents historical data through year-end 2016 and projections through 2030. Key trends from this edition of the report include the following: -Evolution of state RPS programs: Significant RPS-related policy revisions since the start of 2016 include increased RPS targets in DC, MD, MI, NY, RI, and OR; requirements for new wind and solar projects and other major reforms to the RPS procurement process in IL; and a new offshore wind carve-out and solar procurement program in MA. -Historical impacts on renewables development: Roughly half of all growth in U.S. renewable electricity (RE) generation and capacity since 2000 is associated with state RPS requirements. Nationally, the role of RPS policies has diminished over time, representing 44% of all U.S. RE capacity additions in 2016. However, within particular regions, RPS policies continue to play a central role in supporting RE growth, constituting 70-90% of 2016 RE capacity additions in the West, Mid-Atlantic, and Northeast. -Future RPS demand and incremental needs: Meeting RPS demand growth will require roughly a 50% increase in U.S. RE generation by 2030, equating to 55 GW of new RE capacity. To meet future RPS demand, total U.S. RE generation will need to reach 13% of electricity sales by 2030 (compared to 10% today), though other drivers will also continue to influence RE growth. -RPS target achievement to-date: States have generally met their interim RPS targets in recent years, with only a few exceptions reflecting unique state-specific policy designs. -REC pricing trends: Prices for renewable energy certificates (RECs) used to meet general RPS obligations fell in most markets in 2016, as surplus RPS supplies emerged in many regions. Price trends for solar RECs were more varied, with a particularly pronounced drop in MD. -RPS compliance costs and cost caps: RPS compliance costs totaled $3.0 billion in 2015 (the most-recent year for which relatively complete data are available), which equates to 1.6% of average retail electricity bills in RPS states. Though total U.S. RPS compliance costs rose from 2014, future cost growth in most RPS states will be capped by cost containment mechanisms.

In the context of increasing sustainability through renewable energy utilization in Saudi Arabia, this study was motivated by the need to understand the impact of COVID-19 on gym-goers’ attitudes and behaviors towards renewable energy generating using their human kinetic energy (HKE). A comparative analytical study was conducted using a pre-COVID-19 survey (n = 96) and a post-COVID-19 survey (n = 385) of gym-goers in Saudi Arabia. The surveys consisted of closed-ended Likert-type questions that measured participants’ attitudes, behaviors, and willingness related to renewable energy and HKE generation. The results of the Chi-square test of independence showed that there was no significant difference in the participants’ attitudes towards behaviors and awareness regarding renewable energy and HKE generation between the pre-COVID-19 and post-COVID-19 surveys. However, the study revealed an overall promising level of support for sustainable energy and HKE generation among gym-goers. This led to the conclusion that there is potential for the installation of sustainable HKE generation systems in gyms. The main implication of this finding is the need for greater education and awareness raising campaigns as well as incentives to support the implementation of renewable energy solutions. The research contributes to sustainable development and Saudi Vision 2030 by exploring potential opportunities for increasing the share of renewable energy in the overall energy mix and promoting sustainable development.

Unveiling the dynamic impact of energy generation on economic sustainability in Canada: A roadmap towards sustainable development

Between the end of 2015 and mid-2016, Argentina enacted a new renewable energy (RE) promotion regime and launched the so-called RenovAr Program, a public plan that aimed to increase the development of RE generation projects through a series of international bids for power capacity. The proposed goal was to move from 2% RE generation of the total Argentina’s energy mix in 2015, to 20% by 2025. The RenovAr Program, along with RE promotion policies, succeeded in tripling RE generation capacity by 2019. However, over the last years the pace of new RE capacity incorporation has significantly slowed down, making it unlikely to reach the 20% target by 2025. This work analyses the positive aspects of the Argentinean RE promotion regime and the RenovAr Program that led to its rapid success, and it considers the main weaknesses that have prevented the further expansion of RE capacity. This paper concludes that, while designing policies to attract international funding for RE projects is beneficial for increasing RE generation capacity, ensuring its continuous growth in the long-term also requires addressing macroeconomic challenges and sustainable political support.

Nature oriented power generation systems are considered as renewable energy sources. Renewable energy generations are safe to the environment and nature, in terms of minimal radiation and pollution. The space requirement, operational and maintenance cost of renewable energy generation stations are also comparatively lesser than the conventional generating stations. The new form of micro grid energy stations of 230Volt supply attract the small commercial users and the domestic users. The smart grid energy generation is widely employed in the place where the conventional energy supply is not available. Due to its simple construction process, the smart grid renewable energy stations are employed on certain national highways as charging stations for electric vehicles and as a maintenance centre. The motive of the proposed work is to alert the smart grid system with an intelligent algorithm for making an efficient energy generation process on various climatic changes. This reduces the energy wastage in the primary smart grid station and makes the system more reliable on all conditions. The performance of the proposed approach is compared with a traditional smart grid system which yielded a satisfactory outcome.

Synthetic inertia and frequency support assessment from renewable plants in low carbon grids

The rapid growth of cloud computing in cloud datacenters in recent decades greatly increases the brown energy consumption in datacenters, and hence significant increase of carbon emission that negatively impacts on the environment as well as the monetary cost. More and more cloud service providers are adopting renewable energy as the energy supply to offset the consumption of brown energy. Meanwhile, an increasing number of renewable energy generators have been built to meet the needs. However, the instability of the renewable energy cannot guarantee the support to the datacenter and the energy competition of different datacenters may lead to datacenter energy outage. In this paper, we focus on the problem of how to match different renewable energy generators to the datacenters from different cloud providers to minimize the carbon emission, monetary cost, and service level objective (SLO) violation due to renewable energy shortage. The challenges here are that the datacenters may compete in energy requesting, the renewable energy generation is not stable and the decision should be made quickly. There have been no previous efforts devoting to this problem. To solve the problem, we first test several machine learning techniques on long-term prediction accuracy on renewable energy generation and energy demand using real traces and identify SARIMA for the prediction. We then propose a multi-agent reinforcement learning based method (MARL) for each datacenter to determine how much renewable energy to request from each generator based on the predicted results. We also propose a deadline guaranteed job postponement method (DGJP) to postpone executing unurgent jobs upon insufficient renewable energy supply. The trace-driven experiments show that MARL outperforms other methods by increasing up to 35% SLO satisfaction ratio, and reducing up to 19% (0.33 billion dollars in 90 days) total monetary cost and 33% total carbon emission, and DGJP further improves the performance.

In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and energy storages.
\nIn such systems, the uncertain renewable energy will impose unprecedented scheduling challenges. To cope with the fluctuate nature of the renewable energy, an uncertainty model based on renewable energies’ moment statistics is developed. Specifically, we obtain the mean vector and second-order moment matrix according to predictions and field measurements and then define
\nuncertainty set to confine the renewable energy generation. The uncertainty model allows the renewable energy generation distributions to fluctuate within the uncertainty set. We develop chance constraint approximations and robust optimization approaches based on a Chebyshev inequality framework to firstly transform and then solve the scheduling problem. Numerical results based on real-world data traces evaluate the performance bounds of the proposed scheduling scheme. It is shown that the temporal correlation information of the renewable energy within a proper time span can effectively reduce the conservativeness of the solution. Moreover, detailed studies on the impacts of different
\nfactors on the proposed scheme provide some interesting insights which shall be useful for the policy making for the future microgrids.

Towards renewable energy generation and low greenhouse gas emission in high-income countries: Performance of financial development and governance

This dissertation consists of three essays on energy-related state policies and energy transition. Each paragraph below refers to the three abstracts for the three chapters in this dissertation, respectively. The first essay is entitled: “Impacts of State Tax and Resource Ownership Policies on Extraction: Evidence from U.S. Natural Gas Production”. The innovation of combined use of horizontal drilling and hydraulic fracturing technologies during the 2000s has allowed natural gas producers in the United States to extract natural gas and liquids from deep shale formations in a cost-efficient manner. This essay evaluates whether unconventional gas production responds to tax changes, and whether the current tax policies provide support for or dis-incentivize growth in the shale gas sector. State-level data from seven of the largest shale gas-producing states are evaluated from 2007 to 2020 in regression analyses. Based upon panel vector autoregression models, lagged quarterly changes in tax collected have a negative significant, but limited impact on both natural gas and shale gas production in the short run. These findings also contribute to an assessment of the long-term impact of taxation. Considering the long-term impacts, based on the forecast error variance decomposition analysis, the industry gets stabilized, although most of the variation in natural gas production is accounted for by its own shocks and shocks from new producing unconventional wells. In the long-term, tax collected has no explanatory power. An additional analysis utilized a random effects model and found that impact fees do not significantly affect either natural gas or shale gas production relative to severance taxes. Conversely, we find that in states with volume-based tax rates, quarterly natural gas production increases by 2.5% relative to those states with revenue-based tax rates. We also find that the existence of a forced pooling law decreases quarterly natural gas production with an estimated decline of between 2.6% to 3.8% relative to states where no forced pooling law exists. These findings show that the restrictions imposed by forced pooling laws have larger impacts on production compared with different severance tax structures. Essay two is entitled: “Impact of state policies on renewable energy: A county-level spatial panel analysis”. According to EIA (2023), renewable energy (RE) generation capacity in the U.S. has grown to 331,000 MW. Given the numerous state policies incentivizing RE adoption, this research examines their impact on RE generation capacity. Empirical analyses are conducted examining state and county-level policy instruments along with economic, political,

With the augmentation of affordable multimedia wireless gadgets, the ubiquitous availability of the internet access, and the rapid pace of mobile traffic motivate research towards fifth generation (5G) communications to realize energy-efficient cloud radio access networks (C-RAN) with guaranteed quality of experience. Exploiting green energy harvesting for powering the C-RAN substantially alleviates the energy procurement from the utility grid, carbon footprint, and operational expenses. In this paper, we propose a new dynamic point selection coordinated multipoint (DPS CoMP) based load balancing paradigm emphasizing achievable throughput and energy efficiency (EE) by reducing utility grid consumption from a network level perspective. This paper investigates the radio efficiency, EE, and average on-grid energy saving addressing the key challenges of tempo-spatial dynamics of traffic intensity and renewable energy (RE) generation under a wide range of network setup. Endeavoring load balancing technique strives a balance in network utilities such as green energy utilization and user association based on BS coordination technique in a cluster. Provision of cell sleep approach is contemplated for further energy saving by turning off lightly loaded base stations (BSs) during low traffic arrivals. The proposed CoMP based load balancing algorithm proficiently manages resource block allocation to the new users and elevated the energy efficiency over the conventional location and traffic centric mechanisms. Extensive system-level simulations manifest that the suggested framework enables an adjustable trade-off between radio efficiency and EE, and saves 22% on-grid power consumption and increases EE index by 32%. Afterward, an exhaustive comparison of the proposed method with the existing schemes is pledged for further validation highlighting sustainable 5G wireless systems.

The Ministry of the Environment of Japan has promoted the demonstration project named “wide-area operation systems for multiple renewable energy power plants”. The purpose of wide-area operation system is to stabilize the total generation output of photovoltaic (PV) power plants and wind farms using energy storage systems (ESSs) and a controllable renewable energy (RE) generator based on the weather forecasts and online synchronized generation output monitoring. In this paper, detailed control strategy for ESSs and a controllable RE generator is proposed considering the energy/power ratios. The efficacy of proposed control strategy is ascertained through chronological computation simulations using actual observed and forecasted solar irradiance data at Wakkanai PV power plant.

Introduction of Renewable Energy Certificate in the Indian scenario

Nowadays, modern data centers are seeking for importing renewable energy together with conventional energy in order to be more environment-friendly and to reduce operation expenditures. Meanwhile, considering the fact that electricity prices and renewable energy generations are diverse in time and geography, a task scheduling strategy should be designed to ensure the efficient and economic operations of data centers. In this paper, an optimal task placement strategy is presented for geo-distributed data centers powered by mixed renewable and conventional energies with dynamic voltage and frequency scaling technique. We aim at minimizing the total electricity cost and making full use of the renewable energy so as to construct green and economic data centers. The optimal task placement problem is formulated as a mixed integer nonlinear problem (MINLP), in which the quality-of-service constraint is restricted by an M/G/1 queuing model. To tackle the complexity of the MINLP, we first transform it into a tractable form, and then develop an optimal sever activation configuration and task placement algorithm to solve it. The proposed algorithm can obtain the global optimal solution of the electricity minimization problem and meanwhile dramatically reduce the complexity of the problem solving. Finally, evaluations based on real-world traces exhibit impacts of different system parameters on the electricity cost and sever activation configurations, which prove the superiority of our proposed algorithm and provide us some illuminations on how to build cost-effective and eco-friendly data centers.