Water-energy management for demand charges and energy cost optimization of a pumping stations system under a renewable virtual power plant model

The effects of climate change seriously affect agriculture at different latitudes of the planet because periods of drought are intensifying and the availability of water for agricultural irrigation is reducing. In addition, the energy cost associated with pumping water has increased notably in recent years due to, among other reasons, the maximum demand charges that are applied annually according to the contracted demand in each facility. Therefore, very efficient management of both water resources and energy resources is required. This article proposes the integration of water-energy management in a virtual power plant (VPP) model for the optimization of energy costs and maximum demand charges. For the development of the model, a problem related to the optimal operation of electricity generation and demand resources arises, which is formulated as a nonlinear mixed-integer programming model (MINLP). The objective is to maximize the annual operating profit of the VPP. It is worth mentioning that the model is applied to a large irrigation system using real data on consumption and power generation, exclusively renewable. In addition, different scenarios are analyzed to evaluate the variability of the operating profit of the VPP with and without intraday demand management as well as the influence of the wholesale electricity market price on the model. In view of the results obtained, the model that integrates the management of the water-energy binomial increases the self-consumption of renewable energy and saves electricity supply costs

A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation

Volatility and sharp increases in the price of electricity are serious economic problems in the primary sector because they affect modernization investments for irrigation systems in Spain. This paper presents a new virtual power plant (VPP) model that integrates all available full-scale distributed renewable generation technologies. The proposed VPP operates as a single plant in the wholesale electricity market and aims to maximize profit from its operation to meet demand. Two levels of renewable energy integration in the VPP were considered: first, a wind farm and six hydroelectric power plants that inject the generated electricity directly to the distribution network, and second, on-site photovoltaic plants associated with each of the electricity supply points in the system that are designed to prioritize self-consumption. The proposed technical-economic dispatch model was developed as a mixed-integer optimization problem that determines the hourly operation of distributed large-scale renewable generation plants and on-site generation plants. The model was applied to real data from an irrigation system comprising a number of water pumping stations in Aragon (Spain). The results of the VPP model demonstrate the importance of the technical and economic management of all production facilities to significantly reduce grid dependence and final electricity costs

Assessing the impact of investments in Cross-border pipelines on the security of gas supply in the EU

The European Union (EU) is highly dependent on external natural gas supplies and has experienced severe gas cuts in the past, mainly driven by the technical complexity of the high-pressure natural gas system and political instability in some of the supplier countries. Declining indigenous natural gas production and growing demand for gas in the EU has encouraged investments in cross-border transmission capacity to increase the sharing of resources between the member states, particularly in the aftermath of the Russia-Ukraine gas crisis in January 2009. This article models the EU interconnected natural gas system to assess the impact of investments in the gas transmission network by comparing the performance of the system for scenarios of 2009 and 2017, using a mathematical optimization approach. The model uses the technical data of the infrastructures, such as production, storage, regasification, and exchange capacity through cross-border pipelines, and proposes an optimal collaborative strategy which ensures the best possible coverage of overall demand. The actual peak demand situations of the extreme cases of 2009 and 2017 are analyzed under hypothetical supply crises caused by geopolitical or commercial disputes. The application of the proposed methodology leads to results which show that the investments made in this system do not decongest the cross-border pipeline network but improve the demand coverage. Countries such as Spain and Italy experience a lower impact on gas supply due to the variety of mechanisms available to cover their demand. Furthermore, the findings prove that cooperation facilitates the supply of demand in crisis situations

Day-ahead optimal battery operation in islanded hybrid energy systems and its impact on greenhouse gas emissions

This paper proposes a management strategy for the daily operation of an isolated hybrid energy system (HES) using heuristic techniques. Incorporation of heuristic techniques to the optimal scheduling in day-head basis allows us to consider the complex characteristics of a specific battery energy storage system (BESS) and the associated electronic converter efficiency. The proposed approach can determine the discharging time to perform the load peak-shaving in an appropriate manner. A recently proposed version of binary particle swarm optimization (BPSO), which incorporates a time-varying mirrored S-shaped (TVMS) transfer function, is proposed for day-ahead scheduling determination. Day-ahead operation and greenhouse gas (GHG) emissions are studied through different operating conditions. The complexity of the optimization problem depends on the available wind resource and its relationship with load profile. In this regard, TVMS-BPSO has important capabilities for global exploration and local exploitation, which makes it a powerful technique able to provide a high-quality solution comparable to that obtained from a genetic algorithm

Vulnerability assessment of a large electrical grid by new graph theory approach

In previous research a novel methodology to assess structural vulnerability was proposed and applied in IEEE test system and high voltage transmission networks of 94 buses, by using graph theory to investigate various risk scenarios that can trigger cascading failures. In this paper we validate the application of this methodology in larger networks by applying a case study on the transmission network 230 and 400 kV of Mexico. The events of cascading failures are simulated through two elimination strategies: by deliberate attacks on critical nodes or by random errors. Iterations are performed by running successive N-1 contingencies on a network that is constantly changing its structure with the elimination of each node. The power flows are not necessary and only the calculation of the graph statistical parameter geodesic vulnerability is required. This reduces the computation time and leads to a comparative analysis of structural vulnerability

Multitemporal LMDI index decomposition analysis to explain the changes of ACI by the power sector in Latin America and the Caribbean between 1990-2017

This paper analyzes the drivers behind the changes of the Aggregate Carbon Intensity (ACI) of Latin America and the Caribbean (LAC) power sector in five periods between 1990 and 2017. Since 1990 the carbon intensity of the world has been reduced almost 8.8% whereas the carbon intensity of LAC countries only decreased 0.8%. Even though by 2017 the regional carbon intensity is very similar to the one observed by 1990, this index has showed high variability, mainly in the last three years when the ACI of LAC fell from 285 gCO2/kWh in 2015 to 257.7 gCO2/kWh. To understand what happened with the evolution of the carbon intensity in the region, in this paper a Logarithmic Mean Divisia for Index Decomposition Analysis (IDA-LMDI) is carried out to identify the accelerating and attenuating drivers of the ACI behavior along five periods. The proposal outperforms existing studies previously applied to LAC based upon a single period of analysis. Key contributions are introduced by considering the type of fuel used in power plants as well as specific time-series of energy flows and CO2 emissions by country. Results reveal structural reasons for the increase of the ACI in 1995-2003 and 2008-2015, and intensity reasons for the decrease of the ACI in 1990-1995, 2003-2008 and 2015-2017

Effect of olive oil consumption on cardiovascular disease, cancer, type 2 diabetes, and all-cause mortality: A systematic review and meta-analysis

Background: Some large prospective studies on olive oil consumption and risk of chronic disease sug- gested protective effects. Objective: We conducted an outcome-wide systematic review and meta-analysis of prospective cohort studies and randomized controlled trials (RCT) assessing the association between olive oil consumption and the primary risk of 4 different outcomes: cardiovascular disease (CVD), cancer, type 2 diabetes (T2D) or all-cause mortality through January 2022. Methods: Thirty-six studies were included in the systematic review and twenty-seven studies (24 pro- spective cohorts and 3 different reports from one RCT) were assessed in 4 quantitative random-effects meta-analyses. They included a total of 806,203 participants with 49,223 CVD events; 1,285,064 par- ticipants with 58,892 incident cases of cancer; 680,239 participants with 13,389 incident cases of T2D; and 733,420 participants with 174,081 deaths. Olive oil consumption was most frequently measured with validated food frequency questionnaires. Studies follow-up ranged between 3.7 and 28 years. Results: A 16% reduced risk of CVD (relative risk [RR]: 0.84; 95% confidence interval [CI]: 0.76 to 0.94), standardized for every additional olive oil consumption of 25 g/d was found. No significant association with cancer risk was observed (RR: 0.94; 95% CI: 0.86 to 1.03, per 25 g/d). Olive oil consumption was associated with a 22% lower relative risk of T2D (RR: 0.78; 95% CI: 0.69 to 0.87, per 25 g/d) without evidence of heterogeneity. Similarly, it was inversely associated with all-cause mortality (RR: 0.89; 95% CI: 0.85 to 0.93, per 25 g/d). Only the results for T2D were homogeneous. Specific sources of hetero- geneity for the other 3 outcomes were not always apparent. Conclusions: Prospective studies supported a beneficial association of olive oil consumption with CVD, T2D and all-cause mortality, but they did not show any association with cancer risk

Photocatalytic NOx abatement by calcium aluminate cements modified with TiO2: improved NO2 conversion

Photocatalytic activity of TiO2 was studied in two types of Calcium Aluminate Cement (CAC) under two different curing regimes. The effect of the TiO2 addition on the setting time, consistency and mechanical properties of the CACs was evaluated. The abatement of gaseous pollutants (NOx) under UV irradiation was also assessed. These cementitious matrices were found to successfully retain NO2: more abundant presence of aluminates in white cement (w-CAC, iron-lean) helped to better adsorb NO2, thus improving the conversion performance of the catalyst resulting in a larger NOx removal under UV irradiation. As evidenced by XRD, SEM, EDAX and zeta potential analyses, the presence of ferrite in dark cement (d-CAC, iron-reach) induced a certain chemical interaction with TiO2. The experimental findings suggest the formation of new iron titanate phases, namely pseudobrookite. The reduced band-gap energy of these compounds compared with that of TiO2 accounts for the photocatalytic activity of these samples