Enhancing PV Self-consumption within an Energy Community using MILP-based P2P Trading

The high penetration of Distributed Energy Resources (DERs) into the demand side has led to an increase in the number of consumers becoming prosumers. Recently, Peer-to-Peer (P2P) energy trading has gained increased popularity as it is considered an effective approach for managing DERs and offering local market solutions. This paper presents a P2P Energy Management System (EMS) that aims to reduce the absolute net energy exchange with the utility by exploiting two days-ahead energy forecast and allowing the exchange of the surplus energy among prosumers. Mixed-Integer Linear Programming (MILP) is used to schedule the day-ahead household battery energy exchange with the utility and other prosumers. The proposed system is tested using the measured data for a community of six houses located in London, UK. The proposed P2P EMS enhanced the energy independency of the community by reducing the exchanged energy with the utility. The results show that the proposed P2P EMS reduced the household operating costs by up to 18.8% when it is operated as part of the community over four months compared to operating individually. In addition, it reduced the community’s total absolute net energy exchange with the utility by nearly 25.4% compared to a previous state-of-the-art energy management method

Techno-Economic Analysis of Using Solar PV System for Auxiliary Power in a Power Plant

Qatar considered among the countries with the largest per capita electricity consumption. Most of the electricity in Qatar generated in power plants, which use natural gas as the fuel. Qatar has initiated plans to utilize renewable energies to produce 20% of electricity needs by 2030. In this study, the feasibility of using electricity generated by a solar PV system in a power plant is studied through technical and economic analysis. The solar PV system is designed to produce about 15 MW of electricity to feed the power plant auxiliary systems. In this study, a collection of data on power consumption, gas consumption, irradiation and area available for installing solar PV was used to size the maximum capacity of the solar system. Then the cost of installing and utilizing the solar PV system is analyzed. This cost analysis was carried out by first calculating the investment cost of the proposed design and the net present value (NPV). The net present value calculations were done for different scenarios. The first scenario is increasing power plant power capacity. The second scenario is KAHARAMAA to invest 15%, and the third scenario is KAHRAMAA to introduce support initiatives by increasing the cost of electricity by 15 %. The fourth scenario is selling the gas surplus globally. The net present value of all scenarios was found as -$32,717,799, -$23,100,399, -$26,797,148, and$1,353,192 respectively. The study concludes that a solar PV system in Qatar is technically feasible, but a support initiative should be considered. This is due to the low current prices of natural gas and electricity tariffs. This project recommends increasing electricity prices to reflect its actual economic cost. Moreover, the government, along with the power plants, shall invest in utilizing a solar PV system in power plants to reduce gas consumption. So that the saved gas can be exported for income. Those recommendations are proposed to make renewable energy technology adoption in Qatar more feasible

UK energy in a global context: synthesis report

No description supplie

Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study

This paper presents a predictive Energy Management System (EMS), aimed to improve the per-formance of a domestic PV-battery system and maximize self-consumption by minimizing energy exchange with the utility grid. The proposed algorithm facilitates a self-consumption approach, which reduces electricity bills, transmission losses, and the required central generation/storage systems. The proposed EMS uses a com-bination of Fuzzy Logic (FL) and a rule based-algorithm to optimally control the PV-battery system while con-sidering the day-ahead energy forecast including forecast error and the battery State of Health (SOH). The FL maximizes the lifetime of the battery by using SOH and State of Charge (SOC) in decision making algorithm to charge/discharge the battery. The proposed Battery Management System (BMS) has been tested using Active Office Building (AOB) located in Swansea University, UK. Furthermore, it is compared with three recently published methods and with the current BMS utilized in the AOB to show the effectiveness of the proposed technique. The results show that the proposed BMS achieves a saving of 18% in the total energy cost over six months compared to a similar day-ahead forecast-based work. It also achieves a saving up to 95% compared to other methods (with a similar structure) but without a day-ahead forecast-based management. The proposed BMS enhances the battery's lifetime by reducing the average SOC up to 47% compared to the previous methods through avoiding unnecessary charge and discharge cycles. The impact of the PV system size and the battery capacity on the net exchanged energy with the utility grid is also investigated in this study

Development of a life cycle assessment tool for the assessment of food production systems within the energy, water and food nexus

© 2015 The Institution of Chemical Engineers.As the demand for services and products continues to increase in light of rapid population growth, the question of energy, water and food (EWF) security is of increasing importance. The systems representing the three resources are intrinsically connected and, as such, there is a need to develop assessment tools that consider their interdependences. Specifically when evaluating the environmental performance of a food production system, it is necessary to understand its life cycle. The objective of this paper is to introduce an integrated energy, water and food life cycle assessment tool that integrates EWF resources in one robust model and at an appropriate resolution. The nexus modelling tool developed is capable of providing an environmental assessment for food production systems utilising a holistic systems approach as described by a series of subsystems that constitute each of the EWF resources. A case study set in Qatar and characterised by an agriculture sub-system, which includes the production and application of fertilisers and the raising of livestock, a water sub-system represented by mechanical and thermal desalination processes and an energy sub-system, which includes fossil fuel in the form of combined cycle natural gas based energy production and solar renewable energy is used to illustrate the model function. For the nexus system analysed it is demonstrated that the food system is the largest contributor to global warming. The GWP can be reduced by up to 30% through the utilisation of solar energy to substitute fossil fuels, which, however, comes with a significant requirement for land investment

Solar Photovoltaic and Thermal Energy Systems: Current Technology and Future Trends

Solar systems have become very competitive solutions for residential, commercial, and industrial applications for both standalone and grid connected operations. This paper presents an overview of the current status and future perspectives of solar energy (mainly photovoltaic) technology and the required conversion systems. The focus in the paper is put on the current technology, installations challenges, and future expectations. Various aspects related to the global solar market, the photovoltaic (PV) modules cost and technology, and the power electronics converter systems are addressed. Research trends and recommendations for each of the PV system sectors are also discussed.Junta de Andalucía P11-TIC-7070Ministerio de Ciencia e Innovación TEC2016-78430-

Perspectives on Dual-Purpose Smart Water Power Infrastructures for Households in Arid Regions

In hot arid climates, freshwater and power are produced simultaneously through seawater desalination since these regions receive little rainfall. This results in a unique urban water/power cycle that often faces sustainability and resilience challenges. Elsewhere, such challenges have been addressed through smart grid technologies. This chapter explores opportunities and initiatives for implementing smart grid technologies at household level for a case study in Qatar. A functional dual-purpose smart water/power nanogrid is developed. The nanogrid includes multiloop systems for on-site water recycling and on-site power generation based on sustainability concepts. A prototype dual-purpose GSM-based smart water/power nanogrid is assembled and tested in a laboratory. Results of case study implementation show that the proposed nanogrid can reduce energy and water consumptions at household level by 25 and 20%, respectively. Economic analysis shows that implementing the nanogrid at household level has a payback period of 10 years. Hence, larger-scale projects may improve investment paybacks. Extension of the nanogrid into a resilient communal microgrid and/or mesogrid is discussed based on the concept of energy semantics. The modularity of the nanogrid allows the design to be adapted for different scale applications. Perspectives on how the nanogrid can be expanded for large scale applications are outlined

Investigation of Electric Vehicles Contributions in an Optimized Peer-to-Peer Energy Trading System

The rapid increase in integration of Electric Vehicles (EVs) and Renewable Energy Sources (RESs) at the consumption level poses many challenges for network operators. Recently, Peer-to-Peer (P2P) energy trading has been considered as an effective approach for managing RESs, EVs, and providing market solutions. This paper investigates the effect of EVs and shiftable loads on P2P energy trading with enhanced Vehicle to Home (V2H) mode, and proposes an optimized Energy Management Systems aimed to reduce the net energy exchange with the grid. Mixed-integer linear programming (MILP) is used to find optimal energy scheduling for smart houses in a community. Results show that the V2H mode reduces the overall energy costs of each prosumer by up to 23% compared to operating without V2H mode (i.e., EVs act as a load only). It also reduces the overall energy costs of the community by 15% compared to the houses operating without the V2H mode. Moreover, it reduces the absolute net energy exchanged between the community and the grid by 3%, which enhances the energy independence of the community

MILP Optimized Management of Domestic PV-Battery Using Two Days-Ahead Forecasts

This paper proposes an Energy Management System (EMS) for domestic PV-battery applications with the aim of reducing the absolute net energy exchange with the utility grid by utilizing the two days-ahead energy forecasts in the optimization process. A Mixed-Integer Linear Programming (MILP) exploits two days-ahead energy demand and PV generation forecasts to schedule the day-ahead battery energy exchange with both the utility grid and the PV generator. The proposed scheme is tested using the real data of the Active Office Building (AOB) located in Swansea University, UK. Performance comparisons with state-of-the-art and the commercial EMS currently running at the AOB reveal that the proposed EMS increases the self-consumption of PV energy and at the same time reduces the total energy cost. The absolute net energy exchange with the grid and the total operating costs are reduced by 121% and 54% compared to the state-of-the-art and 194% and 8% when compared to the commercial EMS over a six-month period. Furthermore, the results show that the pro-posed method can reduce the energy bill by up to 46%for the same period compared to the state-of-the-art. The paper also investigates the effect of using different objective functions on the performance of the EMS and shows that the proposed EMS operate more efficiently when it is compared with another cost function that directly promotes reducing the absolute net energy exchange

Generation, analysis, and applications of high resolution electricity load profiles in Qatar

Qatar is known to be one of the most energy-intensive countries in the world, yet very little is known about its electricity consumption patterns. In this paper, we unfold the electricity sector in Qatar by presenting high-resolution electricity load profiles, analyzing the unique consumption patterns, and with case studies, showing how load profiles can be used in applications such as demand-side management, renewable energy integration, and mathematical modeling. Moreover, using load profiles, we propose a methodology to estimate the cooling demand which represents the largest portion of the domestic demand. The data used in this study is collected from the official website of the Gulf Cooperation Council Interconnection Authority (GCCIA), validated by the available public datasets, and span a 12 month period from February 2016 to January 2017. The present paper contains the first high-resolution dataset in the GCC region and the results will shed light into future research studies