Toward Green Residential Systems: Is Cooperation The Way Forward?

In this work, we study energy cooperation in a two-user Gaussian multiple access channel (MAC), where the transmitters and the receiver are powered by energy harvesters. In particular, the receiver consumes the harvested energy in decoding the received data and bidirectional energy cooperation is allowed between any two nodes in order to facilitate data transmission and decoding. Our objective is to maximize the number of decoded bits at the receiver, assuming offline knowledge of the channel state information (CSI) and the energy harvesting profiles. We first show that the original problem can be decomposed into an inner problem in which we optimize over the energy transfer variables and an outer problem that characterizes the optimal power allocation. For the inner problem, we derive analytical expressions for the energy transfer variables in terms of the fading coefficients and the energy transfer efficiencies between the nodes. On the other hand, the outer problem is solved using a generalized iterative directional water-filling algorithm. The numerical results show that transmitters and receiver energy cooperation can significantly enhance the system performance.

The progression of a disease may be affected by many risk factors, such as gender, age, and current disease state. Such information is collected and made publically available by published clinical studies, yet combining this information into a disease model remains a challenge. This paper extends the previously published maximum likelihood estimation technique to estimate model parameters from indirect secondary data. Such information is available in the scientific literature so the modeler can access more data when estimating model parameters. The extension to the estimation procedure allows model transitions that depend on different sets of covariates for which secondary data are available. This extension uses a Markov model with transition probabilities stored in multi-dimensional tables accessed by covariate values. The paper uses a set of cases, including a case of cardiovascular disease in diabetes. The cases demonstrate the proposed method with various model variations. To help cope with model multiplicity, a selection method is demonstrated for picking a preferred model according to likelihood and structure criteria.

BackgroundDespite many decades of declining mortality rates in the Western world, cardiovascular disease remains the leading cause of death worldwide. In this research we evaluate the optimal mix of lifestyle, pharmaceutical and population-wide interventions for primary prevention of cardiovascular disease.Methods and FindingsIn a discrete time Markov model we simulate the ischaemic heart disease and stroke outcomes and cost impacts of intervention over the lifetime of all Australian men and women, aged 35 to 84 years, who have never experienced a heart disease or stroke event. Best value for money is achieved by mandating moderate limits on salt in the manufacture of bread, margarine and cereal. A combination of diuretic, calcium channel blocker, ACE inhibitor and low-cost statin, for everyone with at least 5% five-year risk of cardiovascular disease, is also cost-effective, but lifestyle interventions aiming to change risky dietary and exercise behaviours are extremely poor value for money and have little population health benefit.ConclusionsThere is huge potential for improving efficiency in cardiovascular disease prevention in Australia. A tougher approach from Government to mandating limits on salt in processed foods and reducing excessive statin prices, and a shift away from lifestyle counselling to more efficient absolute risk-based prescription of preventive drugs, could cut health care costs while improving population health.

The energy cooperative ènostra provides 100 per cent renewable electricity all over Italy, promotes energy-saving campaigns and supports sustainable electricity procurement. Despite not meeting EU Renewable Energy Community (REC) criteria, it supports collective self-consumption and local REC initiatives, offering tailored consultancy for over 50 projects in Italy. Through the case study of ènostra, this paper explores how energy cooperatives can drive the bottom-up energy transition, as well as how their collaboration with renewable energy communities can foster a just and sustainable energy transition rooted in local territories. By promoting citizen participation and raising awareness of more sustainable energy consumption, renewable energy cooperatives have similar potential to RECs, despite the fact that they are neither incentivised nor promoted by European regulations. This paper discusses the two models (one national cooperative vs several local energy communities), current and potential synergies, as well as highlights regulatory contradictions in light of national decarbonisation goals.

The level of development of renewable energy and energy efficiency is low until now in the Arab States of the Gulf. However, the situation is changing rapidly, as their governments now put climate change on the top of their priorities’ list towards sustainable development. The European Union (EU) is engaged in a process of strengthening energy cooperation with the Gulf Cooperation Council (GCC) countries and supports them in addressing and successfully tackling clean energy issues. To respond to this common interest and provide a supportive instrument for the development of cooperation activities, this Chapter presents a methodological framework for the identification and comparative evaluation of appropriate renewable and energy efficiency solutions towards EU-GCC “clean” energy cooperation. The adopted procedure uses an existing Multi Criteria Decision Making method, giving emphasis on the formulation of a collective interactive process, comprising different rationales for intervention, to elaborate more realistic and transparent outcomes.

Fuel consumption analysis of a residential cogeneration system using a solid oxide fuel cell with regulation of heat to power ratio

The article describes a model that takes into account the impact of unauthorised modes of power transformers owned by consumers on the efficiency of the distribution system operator's power grids. A mathematical model for analysing electricity consumption in power transformers has been developed using real electricity consumption schedules of energy cooperatives and the magnitude of possible abuse is indicated in the case of monthly electricity metering. The influence of unauthorised replacement of transformers on the value of active and reactive power consumption in this transformer is investigated. The article substantiates the limits of daily electricity consumption by an energy cooperative when the actual transformer losses exceed the normalised transformer losses obtained by the calculation method in accordance with the SOU-N EE 40.1-37471933-82_2013. The expediency of reconstruction of metering systems for hourly analysis of electricity consumption is substantiated. Bibl. 14, fig. 4.

In this paper, we consider the operating and capital expenditure (CAPEX) costs of solar-powered additions to infrastructure that is operated from the power grid. The CAPEX costs are those associated with provisioning the solar power components, and are selected using an offline design optimization. Once the solar add-on is designed and deployed, the node incurs ongoing operating expenditure (OPEX) costs associated with the purchase of power grid energy. Lower bounds on cost are derived using a linear programming formulation, where the solar power components are sized using historical solar insolation traces and projected loading data. Different node add-on arrangements are considered, which result in various solar/battery and grid configurations. Three energy scheduling algorithms are then introduced to optimize online OPEX costs. A variety of results are presented that show the extent to which a solar-powered add-on can reduce the total cost. These results also show that the proposed algorithms give performance that is close to the lower bounds in many situations.

For the power grid, the electric vehicle is a new load. The electric vehicles are different from other electrical loads in that they are highly controllable and adjustable. As the rapid development of electric vehicles, the use of residential parking with charging piles for electric vehicles has broad prospects. This paper analyzes the electric vehicle charging mode and the characteristics of charging time, studies the impact of large scale of charge on residential distribution system such as normal power supply, harmonic pollution, peak-valley difference, and put forward some countermeasures in the areas of residential distribution system upgrade, harmonic control, and peak load shifting by use of electric vehicles.

The emerging data traffic demand has caused a massive deployment of network infrastructure, including Base Stations (BSs) and Small Cells (SCs), leading to increased energy consumption and expenditures. However, the network underutilization during low traffic periods enables the Mobile Network Operators (MNOs) to save energy by having their traffic served by third party SCs, thus being able to switch off their BSs. In this paper, we propose a novel market approach to foster the opportunistic utilization of the unexploited SCs capacity, where the MNOs, instead of requesting the maximum capacity to meet their highest traffic expectations, offer a set of bids requesting different resources from the third party SCs at lower costs. Motivated by the conflicting financial interests of the MNOs and the third party, the restricted capacity of the SCs that is not adequate to carry the whole traffic in multi-operator scenarios, and the necessity for energy efficient solutions, we introduce a combinatorial auction framework, which includes i) a bidding strategy, ii) a resource allocation scheme, and iii) a pricing rule. We propose a multiobjective framework as an energy and cost efficient solution for the resource allocation problem, and we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved. In addition, we investigate the conditions under which the MNOs and the third party companies should take part in the proposed auction.

With the increasing demand for solar energy as a renewable source has brought up new challenges in the field of energy. However, one of the main advantages of photovoltaic (PV) power generation technology is that it can be directly connected to the grid power generation system and meet the demand of increasing energy consumption. Large-scale PV grid-connected power generation system put forward new challenges on the stability and control of the power grid and the grid-tied photovoltaic system with an energy storage system. To overcome these problems, the PV grid-tied system consisted of 8 kW PV array with energy storage system is designed, and in this system, the battery components can be coupled with the power grid by AC or DC mode. In addition, the feasibility and flexibility of the maximum power point tracking (MPPT) charge controller are verified through the dynamic model built in the residential solar PV system. Through the feasibility verification of the model control mode and the strategy control, the grid-connected PV system combined with reserve battery storage can effectively improve the stability of the system and reduce the cost of power generation. To analyze the performance of the grid-tied system, some real-time simulations are performed with the help of the system advisor model (SAM) that ensures the satisfactory working of the designed PV grid-tied System.

Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment

This research addresses the critical need for accurate solar energy forecasting in Saudi Arabia’s renewable energy transition. Leveraging the comprehensive Solar Resource Monitoring Stations dataset (2017–2021) from King Abdullah City for Atomic and Renewable Energy, this study presents the first extensive analysis of solar stations across Saudi Arabia with a systematic classification of solar stations into five distinct geographical groups, accounting for regional climate variations and solar radiation patterns. Using meteorological variables, including air conditions, wind patterns, relative humidity, and barometric pressure from 44 stations, we evaluated three machine learning models—Linear Regression (LR), Support Vector Regression (SVR), and Decision Tree (DT)—for predicting daily solar radiation. The results demonstrate that LR achieved superior performance with an RMSE of 139.71 and MAE of 98.32, significantly outperforming other models, particularly the Decision Tree model, which showed the highest error rate RMSE: 504.67. Through detailed regional analysis, identified northern and central regions exhibit consistently high Global Horizontal Irradiance (GHI), marking these areas as optimal locations for residential photovoltaic system deployment. These findings provide valuable insights for optimizing solar resource allocation and integration in Saudi Arabia’s power grid, facilitating the country’s transition to sustainable energy. This study’s results contribute to a better understanding solar resource distribution and variability, supporting informed decision-making for the Kingdom’s sustainable energy objectives.

In this paper, we apply adaptive dynamic programming to the residential energy system control and management, with an emphasis on home battery use connected to power grids. The proposed scheme is built upon a self-learning architecture with only a single critic module instead of the action-critic dual module architecture. The novelty of the present scheme is its ability to improve the performance as it learns and gains more experience in real-time operations under uncertain changes of the environment. Simulation results demonstrate that the proposed scheme can achieve the minimum electricity cost for residential customers.

Presented on Thursday 19 May: Session 24 Greenhouse gas (GHG) inventory assessment, monitoring and auditing is becoming increasingly routine in oil and gas project evaluations. Already, some companies carry an ‘internal’ carbon cost reflected in projected capital and operational expenditure. Early evaluation allows for optimal planning of GHG mitigation and economic analysis inclusive of carbon costs, allaying concerns of investors and lenders. The challenge in evaluating pre-development, however, is the lack of real data and thus, uncertainties in field production. In this paper, we demonstrate the use of a Monte Carlo probabilistic method to better account for uncertainties in production, gas-oil ratio (GOR) and operation loads in a case study of a prospective oil field in offshore Western Australia. We compared the results to the scenario-based deterministic GHG emissions evaluation of the same field and found the deterministic estimates to be extreme representatives of the range of possible emission quantities, due to GOR and production uncertainties. From a breakdown of annual emissions, we also identified the emissions from flaring of excess natural gas to be one of the most significant mitigatable sources of emissions, due to the unexpectedly large production of gas over the project lifetime. Avoiding the flaring of excess gases alone could reduce the project’s emissions by ~44%. Through identifying these key sources and uncertainties, we are able to flag such unexpected, mitigatable sources of emissions at an early stage and provide a representative range of projected emissions, thus assisting the operator to make informed decisions in the field development. To access the presentation click the link on the right. To read the full paper click here