Day-Ahead Offering Strategy In The Market For Concentrating Solar Power Considering Thermoelectric Decoupling By A Compressed Air Energy Storage

Due to limited fossil fuel resources, a growing increase in energy demand and the need to maintain positive environmental effects, concentrating solar power (CSP) plant as a promising technology has driven the world to find new sustainable and competitive methods for energy production. The scheduling capability of a CSP plant equipped with thermal energy storage (TES) surpasses a photovoltaic (PV) unit and augments the sustainability of energy system performance. However, restricting CSP plant application compared to a PV plant due to its high investment is a challenging issue. This paper presents a model to assemble a combined heat and power (CHP) with a CSP plant for enhancing heat utilization and reduce the overall cost of the plant, thus, the CSP benefits proved by researches can be implemented more economically. Moreover, the compressed air energy storage (CAES) is used with a CSP-TES-CHP plant in order that the thermoelectric decoupling of the CHP be facilitated. Therefore, the virtual power plant (VPP) created is a suitable design for large power grids, which can trade heat and electricity in response to the market without restraint by thermoelectric constraint. Furthermore, the day-ahead offering strategy of the VPP is modeled as a mixed integer linear programming (MILP) problem with the goal of maximizing the profit in the market. The simulation results prove the efficiency of the proposed model. The proposed VPP has a 2% increase in profit and a maximum 6% increase in the market electricity price per day compared to the system without CAES

Maximization of Wind Energy Utilization and Flicker Propagation Mitigation Using SC and STATCOM

This paper proposes a novel approach to maximize utilization of wind energy by using a combination of flexible AC transmission system (FACTS) devices, shunt capacitor (SC) and static synchronous compensator (STATCOM). The stochastic nature of wind power is considered through a set of scenarios. After running the real market, the proposed model must be applied by the independent system operator (ISO) to determine the parameters such as the value of the real reserve of each generator. The control procedure of the proposed model is easier and more accelerated due to using SC. Moreover, the proposed method improves the voltage flicker mitigation and power quality parameters due to using STATCOM. The proposed method is applied to IEEE RTS. It is shown that the proposed model affects the total flexibility of the energy system compared to the system without SC and STATCOM in order to enhance effective wind energy utilization

Enhancement of flexibility in multi-energy microgrids considering voltage and congestion improvement: Robust thermal comfort against reserve calls

In recent years, multi-energy microgrid (MEM) has gained increasing interest, which could use clean and efficient electro-thermal resources, multi-energy storages (MESs) and demand response potential to improve the flexibility of MEM. However, maximizing the flexibility potential of MEM and alongside managing the electrical parameters (EPs) is a challenging modeling problem. In this paper, a probabilistic nonlinear model is presented to maximize the flexibility with all the power grid constraints taking into account EPs constraints using power flow. To this end, voltage profile and congestion improvement, robust thermal comfort provision during reserve call and MESs utilization are the key properties of the proposed model. The outcome of suggested model ensures sustainability in the MEM performance, which is an essential feature in modern smart cities. The presented model is applied to a distribution network in the UK and results illustrate how equipment scheduling and demand response leads to observe the EPs limitation and maximizes MEM flexibility. The achieved results show a decrease in MEM revenue (decrease of 34% and 24% without and with reserve commitment, respectively) and in contrast, a significant increase in flexibility compared to non-compliance with EPs constraints

A multi-agent framework for electric vehicles charging power forecast and smart planning of urban parking lots

Abstract This paper proposes a novel stochastic agent-based framework to predict the day-ahead charging demand of electric vehicles (EVs) considering key factors including the initial and final state of charge (SOC), the type of the day, traffic conditions, and weather conditions. The accurate forecast of EVs charging demand enables the proposed model to optimally determine the location of common prime urban parking lots (PLs) including residential, offices, food centers, shopping malls, and public parks. By incorporating both macro-level and micro-level parameters, the agents used in this framework provide significant benefits to all stakeholders, including EV owners, PL operators, PL aggregators, and distribution network operators. Further, the path tracing algorithm is employed to find the nearest PL for the EVs and the probabilistic method is applied to evaluate the uncertainties of driving patterns of EV drivers and the weather conditions. The simulation has been carried out in an agent-based modeling software called NETLOGO with the traffic and weather data of the city of Newcastle Upon Tyne, while the IEEE 33 bus system is mapped on the traffic map of the city. The findings reveal that the total charging demand of EVs is significantly higher on a sunny weekday than on a rainy weekday during peak hours, with an increase of over 150kW. Furthermore, on weekdays higher load demand could be seen during the night time as opposed to weekends where the load demand usually increases during the day time

Evaluation of the diabetes care cascade and compliance with WHO global coverage targets in Iran based on STEPS survey 2021

Abstract This study aimed to investigate the diabetes mellitus (DM) and prediabetes epidemiology, care cascade, and compliance with global coverage targets. We recruited the results of the nationally representative Iran STEPS Survey 2021. Diabetes and prediabetes were two main outcomes. Diabetes awareness, treatment coverage, and glycemic control were calculated for all population with diabetes to investigate the care cascade. Four global coverage targets for diabetes developed by the World Health Organization were adopted to assess the DM diagnosis and control status. Among 18,119 participants, the national prevalence of DM and prediabetes were 14.2% (95% confidence interval 13.4–14.9) and 24.8% (23.9–25.7), respectively. The prevalence of DM treatment coverage was 65.0% (62.4–67.7), while the prevalence of good (HbA1C < 7%) glycemic control was 28.0% (25.0–31.0) among all individuals with diabetes. DM diagnosis and statin use statics were close to global targets (73.3% vs 80%, and 50.1% vs 60%); however, good glycemic control and strict blood pressure control statistics, were much way behind the goals (36.7% vs 80%, and 28.5% vs 80%). A major proportion of the Iranian population are affected by DM and prediabetes, and glycemic control is poorly achieved, indicating a sub-optimal care for diabetes and comorbidities like hypertension