محمد جاسم محمد ال عيسى

Several approaches of energy management systems reduce power consumption of heating demand and electricity storage based on static or dynamic tariffs. However, such methodologies impose uncertainties due to forecasting errors of energy consumption and generation, while evaluating electricity prices. Alternatively, this paper proposes a novel methodology of residential energy management to decrease electricity consumption of space-heating units and grid-connected batteries without incorporating price signals, while maintaining their characteristic operation. The proposed algorithm of energy management develops seasonal calculations of heating load and storage power to achieve energy savings in smart homes based on mixed-integer linear programming, considering photovoltaic electric generation. Power consumption of heating systems is estimated considering heat losses of conduction and ventilation through buildings in addition to other important parameters such as outdoor and indoor temperatures. Charging and discharging patterns of grid-connected batteries are modelled consistent with residential loads. Simulation results show that the proposed algorithm of energy management is able to reduce energy consumption of space-heating loads by 15%, mitigating their environmental impact while keeping their functioning usage. Moreover, the algorithm decreases charging demand of grid-connected batteries by 13%, maintaining their state-of-charge levels between 10 and 90%.

Abstract Intensive charging and discharging modes of batteries cause an adverse effect on state of health of grid-integrated energy storage batteries (GIESBs). Furthermore, uncoordinated patterns of charging and discharging the GIESBs are able to expand voltage profiles of power grids beyond their limits, while considering extra generation from photovoltaic arrays (PVs) and wind turbines (WTs). Therefore, a decentralized power management scheme is proposed in this paper to mitigate such impact on grid voltages and to improve the performance of the GIESBs with PVs and WTs. The proposed scheme develops three clusters of different objective functions to coordinate charging and discharging cycles of the GIESBs based on mixed integer linear programming, considering state of charge (SoC) and depth of discharge (DoD) of the batteries. The first cluster minimizes the power-charging cost of the GIESBs by utilizing time of use tariffs. The second cluster minimizes the charging power of the GIESBs, employing per unit generation from PVs and WTs, whereas the third cluster minimizes the discharging power of the GIESBs, using per unit consumption of residential loads. Simulation results of six scenarios show that, although the proposed scheme of the first cluster reduces operational costs of the GIESBs, it cannot maintain system voltages within the limitations. With the second and third clusters, the proposed scheme exhibits a better performance in stabilizing grid voltages, while maintaining the SoC and DoD within their boundaries to underpin state of health of the batteries.

This paper investigates the effect of photovoltaic panels (PVs) and electric vehicles (EVs) on power quality of electrical distribution systems, while proposing an approach of demand-side management (DSM). Electricity generation of PVs is estimated based on technical parameters and weather data, whereas electricity consumption of EVs is evaluated using travel data of conventional cars. Calculations of three-phase power flow are developed in this study to assess the impact of PVs and EVs on voltage magnitude and voltage unbalance of residential grids. Simulation results of different case studies show that additional power generation of PVs increases voltage magnitude. However, uncoordinated electricity consumption of charging EVs degrades voltage unbalance. Therefore, a strategy of DSM is proposed to coordinate EV charging using deterministic programming, while considering historical data of system components. The proposed scheme of DSM is able to improve voltage quality by re-scheduling EV consumption in line with PV generation, postponing upgrading requirements of power grids.

Energy management systems modify typical electricity consumption of some appliances to enhance energy efficiency, while considering operational characteristics of such devices. This paper proposes a scheme of home energy management (HEM) to adapt power consumption and generation of thermostatically controlled loads (TCLs) and photovoltaic-battery systems based on their operating conditions. TCLs of air conditioning systems are initially evaluated considering variable in-home temperatures, whereas their energy consumption is estimated using degree-days. Meanwhile, power generation of the photovoltaic-battery system is calculated according to technical parameters, solar irradiances and ambient temperatures. The proposed scheme of HEM system optimizes power consumption of TCLs using linear programming, sustaining the in-house temperature within its threshold. Charging and dis-charging power of the photovoltaic-battery system is accordingly re-scheduled while monitoring its battery state of charge. Simulation results of different case studies show that the proposed HEM scheme reduces energy consumption of the TCLs and photovoltaic-battery systems by 30%, while maintaining customer's quality of experience. Moreover, the proposed strategy prevents batteries from being intensely charged and dis-charged to improve their performance and to expand their lifecycle. Consequently, the proposed HEM plan is able to decrease operational and maintenance costs of such systems.

.

This paper proposes an emergency-based demand response (DR) controller of domestic heat pump (DHP) units based on an estimated frequency of the UK electricity in 2035. The normal pattern of DHP demand is adjusted to maintain system frequency within its limit using a linear model of power and temperature inside low-carbon houses, while considering consumer comfort. Simulation results show that the proposed DR design of static/dynamic frequency-controlled DHPs will increase the amount of power reserve by 75% and the amount of electricity market by 70%, as compared to their values of the current frequency response by flexible loads.

للاطلاع على محاضرات تحليل نظم القدرة باستخدام الرابط الالكتروني https://elearning7.uokufa.edu.iq/eng/course/search.php?areaids=core_course-course&q=Power+system+analysis