Assessment of energy storage systems as a reserve provider in stochastic network constrained unit commitment

Recently, the provision of the reserve from energy storage systems (ESSs) is introduced as a source for ancillary services to address the uncertainties of renewable power generations. The performance of ESSs is analysed while they are applied as a provider of regulation reserves. It has been revealed that previous stochastic models neglect the impact of corrective dispatches, related to the provision of regulation reserves, on the energy level stored in the ESSs, which can lead to large deviations. This study coordinates the stored energy of ESSs to be feasible regarding the dispatches in the base schedule and rescheduling within scenarios. Also, the wind speed fluctuations are considered as the source of uncertainty, and scenarios of wind energy are generated using the Weibull distribution function. The IEEE 24-Bus standard test system is applied for the examination of the proposed model. The results show that the proposed model can manage the performance of ESSs in rescheduling within scenarios, while the coordinated reserve provision of ESSs can remove the concerns about insufficient stored energy of ESSs

Emerging technologies for the energy systems of the future

The way the world gets its energy is undergoing a rapid transition, driven by both the increased urgency of decarbonizing energy systems and the plummeting costs of renewable energy technologies. The road to the future will not be easy, and indeed, new technologies, markets, architectures, infrastructures, actors, and business models should be developed, and major changes will be required in the regulation of the energy systems to further support new business models and new consumption patterns. Such a transition requires rethinking every single aspect of energy systems, starting from the way energy is produced and harvested to the way that we dispatch and use it. In this area, it is also imperative to understand how to control and manage existing and emerging technologies to enhance the energy economy and efficiency by active participation in different services required by energy networks

Value of regional constraint management services of vector-bridging systems in a heavily constrained network

While a lot of countries put renewable energy sources at the heart of their decarbonization strategies with directed incentive mechanisms, the variability of the renewable energy sources, remains a major challenge for electricity system operators in ensuring the security of supply. This challenge is particularly onerous when there is a coincidence between this variability and congestion of the tie-lines. Renewable generation spillage often leads to constraints being placed on the output of renewable energy sources. This situation causes a significant cost for electricity system operators due to the need for constraint payments to be made to renewable generations. These increased costs will ultimately be recovered from energy customers. Maintaining the balance in the aforementioned decarbonization, security of supply and affordability is a challenge that constitutes the energy trilemma. The integration of electric power systems with other energy infrastructures, e.g., natural gas, could be a promising solution for achieving a balanced performance in the energy trilemma, controlling the fluctuation of renewable energy sources, and increasing the flexibility of the integrated systems. Considering this, a hybrid bridging-operational framework based on the vector-bridging system concept is proposed. Also, a day-ahead integrated scheduling model is proposed that optimizes the integrated operation by considering the constraint payment costs in a linear optimization model. Simulation results on a large test system indicated that the hybrid bridging-operational framework could reduce the total cost of the congested system by 65% and release up to 10% of the pipeline capacities while harvesting the wind generation and removing constraint payments to wind generators

A technical assessment on photovoltaic power generation under varying weather profile – Northumbria university pilot

The output from Photovoltaic (PV) system’s is mostly dependent on the weather of the area in which it is installed and it is greatly influenced by the amount of sunlight, irradiance intensity and the time of day (sunny or daylight hours). Consequently, climate in the United Kingdom (UK) does not allow the production of consistent power throughout the year. This paper discusses and analyses the realistic weather data in the UK to provide the accurate climate factor for solar energy production. The present study aims to help decision-makers take reasonable steps to provide solar energy production solutions while considering the weather benefits and as well as abnormalities. This paper tends to provide a comparative analysis of actual and theoretical performance conducted over a one-year monitoring cycle. The paper starts with an overview of performance co-efficient followed by the details of energy produced during various weather periods to investigate the reliability of depending solely on solar. A 75% decline in winter output relative to the summer and an energy generation of only 1.8% on the darkest day of the year compared to the brightest day are reported

Day-ahead economic dispatch of coupled desalinated water and power grids with participation of compressed air energy storages

Nowadays, water and electricity are closely interdependent essential sources in human life that affect socio-economic growth and prosperity. In other words, electricity is a fundamental source to supply a seawater desalination process, while fresh water is used for cooling this power plant. Therefore, mutual vulnerability of water treatment and power generation systems is growing because of increased potable water and electricity demands especially during extremely-hot summer days. Hence, this paper presents a novel framework for optimal short-term scheduling of water-power nexus aiming to minimize total seawater desalination and electricity procurement cost while satisfying all operational constraints of conventional thermal power plants, co-producers and desalination units. Moreover, advanced adiabatic compressed air energy storage (CAES) with no need to fossil fuels can participate in energy procurement process by optimal charging during off-peak periods and discharging at peak load hours. A mixed integer non-linear programming (MINLP) problem is solved under general algebraic mathematical modeling system to minimize total water treatment cost of water only units and co-producers, total fuel cost of thermal power plants and co-generators. Ramp up and down rates, water and power generation capacities and balance criteria have been considered as optimization constraints. It is found that without co-optimization of desalination and power production plants, load-generation mismatch occurs in both water and energy networks. By incorporating CAES in water-power grids, total fuel cost of thermal units and co-producers reduce from $1222.3 and$24933.2 to $1174.8 and$24636.8, respectively. In other words, application of CAES results in $343.9 cost saving in benchmark water-power hybrid grid

Exploring potential gains of mobile sector-coupling energy systems in heavily constrained networks

The coincidence of high levels of variable, non-dispatchable generation from renewable energy sources (RESs) and congested electricity networks imposes significant constraint payments (CP) on electricity system operators (ESOs) which ultimately is charged to the customers. This paper is inspired by this challenge and proposes an integrated electricity, gas, and transportation energy system taking advantage of power-to-gas (P2G) facilities and electricity/gas storage devices to enhance operational efficiency. It proposes mobile gas storage systems (MGSs) that can store and carry liquid hydrogen or liquefied natural gas (LNG) to the load points or remote locations without access to the gas network. So, the green energy of RESs in the form of gases can be injected, transported, and reutilized in the natural gas network or stored in MGS facilities. Besides, the mobile electricity storage system (MES) can directly store the redundant electricity produced by RESs, and the railway transportation system carries both the MESs and MGSs to the load point of electrical and gas systems. The proposed model reflects CP to wind in the marketing phase and considers incentives for the hydrogen-burning generators. Also, a stochastic platform is employed to capture the inherent uncertainties in the predicted values of the load and RESs’ generation. The model is formulated as a mixed-integer second-order cone programming problem and tested on an IEEE 118-bus system integrated with a 14-node gas network and a railway system. The result shows that employing the multi-vector energy system (MVES) elements reduces the total operational cost by 47%, and the CP to wind is reduced by 99.8% by absorbing almost the whole green energy of wind farms while relieving congestion in the electrical grid

A new false data injection attack detection model for cyberattack resilient energy forecasting

As power systems are gradually evolving into more efficient and intelligent cyber-physical energy systems with the large-scale penetration of renewable energies and information technology, they become increasingly reliant on a more accurate forecasting. The accuracy and generalizability of the forecasting rest to a great extent upon the data quality, which is very susceptible to cyberattacks. False data injection (FDI) attacks constitute a class of cyberattacks that could maliciously alter a large portion of supposedly-protected data, which may not be easily detected by existing operational practices, thereby deteriorating the forecasting performance causing catastrophic consequences in the power system. This paper proposes a novel data-driven FDI attack detection mechanism to automatically detect the intrusions and thus enrich the reliability and resilience of the energy forecasting systems. The proposed mechanism is based on cross-validation and least-squares providing accurate detection with low computational cost and high scalability without utilizing the model and parameters of the system. Effectiveness of the proposed detector is corroborated through six representative tree-based wind power forecasting models including decision tree, bagging, random forest, boosting, gradient boosting, and XGboost. Experiments indicate that corrupted data is properly located and removed, whereby the accuracy and generalizability of the final forecasts is recovered

Decomposition-based stacked bagging boosting ensemble for dynamic line rating forecasting

Effective exploitation of overhead transmission lines needs reliable and precise dynamic line rating forecasting. High-accuracy dynamic line rating forecasting, in particular, is an important short-term method for coping with grid congestion, enhancing grid stability, and accommodating high renewable energy penetration. Due to the non-stationarity and stochasticity of the meteorological variables, a single model is often not sufficient to accurately predict the dynamic line rating. Herein, a new stacked bagging boosting ensemble is developed based on multivariate empirical mode decomposition to overcome single models' restrictions and increase the dynamic line rating forecasting performance. The developed ensemble is utilized on the data gathered from a 400 kV aluminum conductor steel-reinforced overhead power line with a length of 32.85 Km between Ghadamgah and Binalood wind farms, located in the northeast of Iran. The simulation results substantiate that the proposed ensemble can capture meteorological variables' non-linear characteristics, yielding more accurate yet to noisy data forecasts than single forecasting models

Active building as an energy system: concept, challenges, and outlook

Over the last decades, environmental concerns and the global tendency to reduce the use of fossil fuels and replacing them with renewable energy sources (RESs) to face the increasing rate of greenhouse gas (GHG) emissions have increased. Buildings consume a significant amount of energy and therefore, they are responsible for a noticeable part of the total GHG emission. Thus, when we talk about decarbonization of the energy systems, buildings are an important sector of the energy system that needs to be considered. Using RESs, smart technologies, and information and communication technologies along with the improvement in energy efficiency, are a number of endeavors to increase the role of building on the way toward decarbonization. In the new environment, the buildings are not passive players of the energy systems and they are able to take an active role and participate in the energy-efficient operation. While they are able to manage their resources and serve the local energy requirements of the residents in the best possible manner, they can participate in the energy and balancing markets and support the network operators as a service provider. In this paper, we present a comprehensive review of active buildings’ concept, challenges and outlook to pave the way for the researchers from academia and industry who want to start working in this area