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حياة الحيوان
يعرف الكتاب متى ظهرت الثدييات على الأرض وما أنواع تضع بيوضا ولماذا نقول أن الشرغوف يتحول وما رخويات تعيش في قوقعة وسيعرف كيف يحفر الخلد نفقه وبماذا يبطن القندس قعر عشه وكم سنا يمكن للقرش أن يستهلك في حياته وماذا تطرح البومة الصماء بعد أن تأكل وما فراشة تتنكر في زي ورقة وماذا تشبه بقع النمر ولماذا تتألف الديدان اللامعة وهذا قليل مما سيتعرف إليه الصغار عن عالم الحيوان من خلال هذا الكتاب الذي يوقظ بمعلوماته حس المعرفة في الصغير.
BOOK
Single-Phase PV Power Injection Limit due to Voltage Unbalances Applied to an Urban Reference Network Using Real-Time Simulation
As photovoltaic (PV) penetration increases in low-voltage distribution networks, voltage variation may become a problem. This is particularly important in residential single-phase systems, due to voltage unbalances created by the inflow of points in the network. The existing literature frequently refers to three-phase systems focusing on losses and voltage variations. Many studies tend to use case studies whose conclusions are difficult to replicate and generalise. As levels of residential PV rise, single-phase PV power injection levels, before voltage unbalances reach standard limits, become important to be investigated. In this study, an urban European reference network is considered, and using a real-time digital simulator, different levels of PV penetration are simulated. PV systems are connected to the same phase (unbalanced case), and are also evenly phase-distributed (balanced case). Considering a 2–3% unbalance limit, approximately 3.5–4.6 kW could be injected in every bus in an unbalanced scenario. With a balanced PV distribution, the power injected could reach 10–13 kW per bus. Buses closer to the power transformer allow higher power connections, due to cable distances and inferior voltage drops. Feeder length, loads considered during simulation, and cable shunt capacitance reactance influence the results the most.
Journal Article
Price forecasting for the balancing energy market using machine-learning regression
The importance of price forecasting has gained attention over the last few years, with the growth of aggregators and the general opening of the European electricity markets. Market participants manage a tradeoff between, bidding in a lower price market (day-ahead), but with typically higher volume, or aiming for a lower volume market but with potentially higher returns (balance energy market). Companies try to forecast the extremes of revenues or prices, in order to manage risk and opportunity, assigning their assets in an optimal way. It is thought that in general, electricity markets have quasi-deterministic principles, rather than being based on speculation, hence the desire to forecast the price based on variables that can describe the outcome of the market. Many studies address this problem from a statistical approach or by performing multiple-variable regressions, but they very often focus only on the time series analysis. In 2019, the Loss of Load Probability (LOLP) was made available in the UK for the first time. Taking this opportunity, this study focusses on five LOLP variables (with different time-ahead estimations) and other quasi-deterministic variables, to explain the price behavior of a multi-variable regression model. These include base production, system load, solar and wind generation, seasonality, day-ahead price and imbalance volume contributions. Three machine-learning algorithms were applied to test for performance, Gradient Boosting (GB), Random Forest (RF) and XGBoost. XGBoost presented higher performance and so it was chosen for the implementation of the real time forecast step. The model returns a Mean Absolute Error (MAE) of 7.89 £/MWh, a coefficient of determination (R2 score) of 76.8% and a Mean Squared Error (MSE) of 124.74. The variables that contribute the most to the model are the Net Imbalance Volume, the LOLP (aggregated), the month and the De-rated margins (aggregated) with 28.6%, 27.5%, 14.0%, and 8.9% of weight on feature importance respectively.
Publication
Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis
The interest in modeling the operation of large-scale battery energy storage systems (BESS) for analyzing power grid applications is rising. This is due to the increasing storage capacity installed in power systems for providing ancillary services and supporting nonprogrammable renewable energy sources (RES). BESS numerical models suitable for grid-connected applications must offer a trade-off, keeping a high accuracy even with limited computational effort. Moreover, they are asked to be viable in modeling for real-life equipment, and not just accurate in the simulation of the electrochemical section. The aim of this study is to develop a numerical model for the analysis of the grid-connected BESS operation; the main goal of the proposal is to have a test protocol based on standard equipment and just based on charge/discharge tests, i.e., a procedure viable for a BESS owner without theoretical skills in electrochemistry or lab procedures, and not requiring the ability to disassemble the BESS in order to test each individual component. The BESS model developed is characterized by an experimental campaign. The test procedure itself is framed in the context of this study and adopted for the experimental campaign on a commercial large-scale BESS. Once the model is characterized by the experimental parameters, it undergoes the verification and validation process by testing its accuracy in simulating the provision of frequency regulation. A case study is presented for the sake of presenting a potential application of the model. The procedure developed and validated is replicable in any other facility, due to the low complexity of the proposed experimental set. This could help stakeholders to accurately simulate several layouts of network services.
Publication
Demand response impact evaluation: A review of methods for estimating the customer baseline load
Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (“baseline”) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost
Publication
EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions
The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption; in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed.
Journal Article
Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector
Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers.
Journal Article
Renewable Energy Community Pairing Methodology Using Statistical Learning Applied to Georeferenced Energy Profiles
Renewable energy communities (REC) are bound to play a crucial role in the energy transition, as their role, activities, and legal forms become clearer, and their dissemination becomes larger. Even though their mass grid integration, is regarded with high expectations, their diffusion, however, has not been an easy task. Its legal form and success, entail responsibilities, prospects, trust, and synergies to be explored between its members, whose collective dynamics should aim for optimal operation. In this regard, the pairing methodology of potential participants ahead of asset dimensioning seems to have been overlooked. This article presents a methodology for pairing consumers, based on their georeferenced load consumptions. A case study in an area of Porto (Asprela) was used to test the methodology. QGIS is used as a geo-representation tool and its PlanHeat plugin for district characterization support. A supervised statistical learning approach is used to identify the feature importance of an overall district energy consumption profile. With the main variables identified, the methodology applies standard K-means and Dynamic Time Warping clustering, from which, users from different clusters should be paired to explore PV as the main generation asset. To validate the assumption that this complementarity of load diagrams could decrease the total surplus of a typical PV generation, 18 pairings were tested. Results show that, even though it is not true that all pairings from different clusters lead to lower surplus, on average, this seems to be the trend. From the sample analyzed a maximum of 36% and an average of 12% less PV surplus generation is observed.
Journal Article
Hybrid Energy Storage System Dispatch Optimization for Cost and Environmental Impact Analysis
Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating energy storage systems (ESSs) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This article compares the performance of hybrid energy storage systems (HESSs) to a single battery, evaluating their energy supply cost and environmental impact through optimization problems. The optimization model is based on a MILP incorporating the energy and degradation terms. It generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load. Seven technologies are assessed, with an example applied to an industrial site combining a vanadium redox flow battery (VRFB) and lithium battery considering the demand of a local load (building). The results indicate that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.
Journal Article
Evaluating the impact of nitisinone at mosquito-lethal doses on Lutzomyia longipalpis
Nitisinone, a systemic inhibitor of tyrosine catabolism, has recently emerged as a promising endectocide with demonstrated lethality against mosquitoes and tsetse flies. To assess its efficacy against Lutzomyia longipalpis , an important vector of Leishmania infantum —the causative agent of zoonotic visceral leishmaniasis prevalent in the Mediterranean basin, Latin America, and parts of the Middle East—, we exposed sand flies to a concentration previously shown to be lethal to Anopheles mosquitoes (250 ng/mL). Survival was monitored over 14 days and showed no significant difference between treated and control groups, indicating a lack of insecticidal effect at this dose. These findings suggest that Lu. longipalpis exhibits tolerance to nitisinone, likely due to species-specific metabolic differences. Although nitisinone remains a promising tool for systemic vector control, its effectiveness against sand flies may require higher doses or alternative compounds.
Journal Article