Non-programmable Renewable Energy Sources Research Articles

Power-to-Gas for energy system flexibility under uncertainty in demand, production and price

The growing penetration of non-programmable renewable energy sources and the consequent fluctuations in energy prices and availability lead to the need to enhance energy system flexibility and synergies between different energy vectors. This can be reached through sector integration. Among the most relevant technologies used for this purpose, Power-to-Gas systems allow excess renewable electricity to be converted directly into fuels that can be then stored or used. A smart energy system, however, which includes these innovative solutions, requires intelligent management methods to optimize its operation. This work investigates the operational strategy of energy systems integrated with Power-to-Gas solutions for seasonal storage, by developing an optimization model for the system, formulated as Mixed-Integer Linear Programming problem. The algorithm tackles the uncertain nature of future disturbances, such as energy needs, generation and price using two-stage stochastic programming. The algorithm is tested on grid-connected and 100% renewable energy supply case studies. The novel stochastic algorithm allows a more robust optimization compared to a deterministic optimization, and system management is ensured under several future disturbances realization. Furthermore, the integration of Power-to-Gas solutions warrants the energy security of the energy systems and acts as a buffer to forestall unpredictable behavior of the disturbances.

Improvement of energy self-sufficiency in residential buildings by using solar-assisted heat pumps and thermal and electrical storage

To reach the EU 2030 goal of reducing greenhouse gas emissions targets, many apartments within residential buildings can be equipped with rooftop or building-integrated PV systems.However, as non-programmable renewable energy sources (solar, wind) are characterized by uncertainty and fluctuation, it is very difficult to match the supply with the demand for the building’s energy needs. Thermal and electric energy storage play a fundamental role in maximizing self-consumption, reducing the difference between peaks and valleys of the energy demand, and improving the electrical system's flexibility. In this study, the performances of an energy system composed of an electric heat pump (HP) fed by a PV plant and both thermal and electric storage are investigated. An innovative logic of the charge and discharge of the two storages as a function of energy generation and demand has been developed with the aim to optimize the energetic self-sufficiency of typical residential buildings. The results of the analyses carried out evidence that the system configurations with a thermal storage of about 1.000 L and an electrical storage of 5.0 kWh allow achieving rates of self-consumption and self-sufficiency of about 80%, which are 3 times higher than that one achievable by an energy system without storage. Moreover, this system configuration reduces dramatically the power exchange with the grid. The outcomes of this study are useful to provide indications for the design of the storage in combination with a solar-assisted heat pump system avoiding the recurrent praxis of oversizing of more than 100% of the electric storage.

Development, validation, and testing of advanced mathematical models for the optimization of BESS operation

Current decarbonization strategies are driven by the fast-paced diffusion of non-programmable renewable energy sources (NP-RESs), mainly through solar and wind power generation. Energy storage technologies are emerging as key solutions for coping with the variability and low-inertia characteristics of NP-RESs. Particularly, battery energy storage systems (BESS) are diffusing more widely for both behind-the-meter (BTM) and utility-scale applications. In this context, we still lack a shared solution on how to proceed from the on-field data collected about the performance of BESS to reliable and fast mathematical formulations for operational optimization. This study provides a validated modeling framework that can be exploited during or after BESS commissioning to (i) identify and derive the useful parameters to characterize BESS performances, (ii) formalize them in a mathematical formulation while being aware of its specific trade-off between accuracy and computational effort, and (iii) exploit the selected BESS model within a multi-energy system optimization problem. We discuss three different modeling approaches that we developed for optimizing BESS operation, with each providing a different balance between modeling accuracy and computational effort. These three mathematical models were validated against a numerical simulation model based on on-field performance data, and they were eventually tested on a reference case study. The results indicate that it is possible to restrict the average error in estimating BESS efficiency while simultaneously limiting the computational effort of the model. Regarding the operation of BESS, the conducted simulations demonstrate that an approximate BESS model may result in an overestimation of the expected revenues.

Battery and Flywheel hybridization of a reversible Pumped-Storage Hydro Power Plant for wear and tear reduction

Energy security and environmental challenges are some of the drivers for increasing the electricity generation from non-programmable Renewable Energy Sources (RES), adding pressure to the grid, especially if located in weakly connected (or isolated) islands, like Sardinia. Variable-speed Pumped Storage Hydro Power (PSHP) can offer a high degree of flexibility in providing ancillary services (namely primary and secondary regulations), but due to the hydro-mechanical nature of the equipment, sudden variations in the power output cause wear and tear. Other energy storage devices cannot compete with PSHP in terms of energy and power availability. The aim of this research is to assess the benefits derived from the hybridization of a PSHP with Battery Energy Storage System (BESS) and Flywheel Energy Storage System (FESS), to be installed in the Sardinia island (Italy). A dynamic model of the hybrid plant was made in MATLAB–Simulink® environment. A detailed model of the variable-speed pump-turbine was obtained from experimental data, and a simplified model of a fixed-speed turbine was produced. A detailed FESS model was provided by CIEMAT (Madrid, Spain) and a simplified BESS model was included. A dedicated control strategy to manage the power flows and accounting for State Of Charge (SOC) control, was implemented. A total of 100 combinations of BESS and FESS powers were taken into account, and the control strategy was calibrated for each one of them. The plant was simulated open-loop over a 3600s time period, feeding historical frequency and Automatic Generation Control (AGC) data. The simulations covered three PSHP operation modes: variable/fixed-speed turbine and variable-speed pump, and with/without hybridization. The performances of the hybridization were evaluated with wear and tear indicators for the PSHP (distance travelled by and number of movements of the wicket gate for turbine, fluctuations of the shaft torque for the pump) and capacity loss (life consumption) for the BESS. The results show that all the combinations of BESS and FESS powers result in the reduction of both the travelled distance and number of movements of the guide vanes. The best hybrid combination for the PSHP does not affect the BESS life consumption, which still is always in an acceptable range. A comparison between the non-hybrid variable-speed turbine and the hybrid fixed-speed counterpart shows that the electric powers do not differ substantially, but the hybridization smooths the movement of the guide vanes. The pump torque fluctuations sharply decrease with the hybridization, but more research is needed to validate that the change in the fluctuation index corresponds to a physical phenomenon. Overall, the hybridization improves the plant performances in terms of wear and tear reduction, and the presence of an additional FESS benefits both the BESS and the PSHP. The results also highlight the necessity for more research in variable-speed pumps providing ancillary services, and their impact.

ACAES systems to enhance the self-consumption rate of renewable electricity in sustainable energy communities

This paper aims to evaluate the optimal configuration of an Adiabatic Compressed Air Energy Storage (ACAES) system designed to achieve the best matching between power production from non-programmable Renewable Energy Sources (RES) power plants and power demand from final users. The electrical energy demand of a small town, with a maximum power load of about 10 MW, has been considered as case study. The electrical energy can be supplied by both a photovoltaic (PV) power plant and the grid. For the ACAES system, different sizes for compressor, turbine, thermal energy storage (TES) system and air storage reservoir have been evaluated by varying the air mass flow rate of turbomachines and the charging and discharging duration times, to enhance the share of the PV energy supplied to the end user. The best performance is achieved with a PV power plant rated at about 35 MW and an ACAES section characterized by a compressor/turbine rated power of about 25-35% of the maximum power load of the end user, with a charging time of about 10 hours and a discharging time of about 20 hours. The average round-trip efficiency of the ACAES system is around 70%. On the overall, the integrated PV-ACAES system allows to cover 66% of the yearly electrical energy demand.

Dynamic Modeling of a PEM Fuel Cell Power Plant for Flexibility Optimization and Grid Support

The transition toward high shares of non-programmable renewable energy sources in the power grid requires an increase in the grid flexibility to guarantee grid reliability and stability. This work, developed within the EU project Grasshopper, identifies hydrogen Fuel Cell (FC) power plants, based on low temperature PEM cells, as a source of flexibility for the power grid. A dynamic numerical model of the flexible FC system is developed and tested against experimental data from a 100-kW pilot plant, built within the Grasshopper project. The model is then applied to assess the flexible performance of a 1 MW system in order to optimize the scale-up of the pilot plant to the MW-size. Simulations of load-following operation show the flexibility of the plant, which can ramp up and down with a ramp rate depending only on an externally imposed limit. Warm-up simulations allow proposing solutions to limit the warm-up time. Of main importance are the minimization of the water inventory in the system and the construction of a compact system, which minimizes the distance between the components.

A New Probabilistic Ensemble Method for an Enhanced Day-Ahead PV Power Forecast

The penetration of nonprogrammable renewable energy sources, namely wind and solar technology, has greatly increased in the last decades and nowadays the shift toward green energy sources represents a priority worldwide. The high variability of the primary source challenges the grid operators in ensuring the stability and reliability of the electric grid. Machine learning algorithms, and in particular artificial neural networks, are one of the most reliable methods for photovoltaic (PV) energy production forecast. This article proposes a new ensemble method based on the probabilistic distribution of the trials, the probabilistic ensemble method (PEM). The proposed method has been tested on a three-years real case study, where the available days have been clustered according to the solar irradiation forecast. The days where the worst performance, in terms of nRMSE, was recorded mostly belonged to the totally cloudy days class, that has been therefore selected for the analysis. The PEM has been compared with the ensemble based on the mean value, achieving an improvement in the nRMSE metric up to 4.79% in 2017 in the totally cloudy days class.

Dams and territories: ITCOLD’s initiatives to promote proactive dialogue

In Italy there are over 530 large dams mainly for energy production and water supply. At present, the multipurpose use of water resources is strongly influenced by problems related to the aging of dams and the ongoing climate change which affects the water availability. On the other hand, the European energy and climate directives require to preserve the existing hydropower generation and to increase storage capacity to better exploit non-programmable renewable energy sources. These choices require the identification of methods and actions to deal with the complex relationship between the dams and their host territories which represents a critical point on the way to attain the energy and climate goals considering that part of the media and public opinion often have a negative view on the role of dams and reservoirs. These considerations are at the basis of the regional initiatives “Dams and territory” promoted by ITCOLD (Italian Committee on Large Dams) which since 2014 has organized annual workshops whose aim is to promote dialogue between dam owners and interested parties to identify a point of balance between the various interests that sometimes conflict with each other. The paper represents an updating of the work presented during the 10th ICOLD European Club Symposium held in Antalya (Turkey) on October 2016 and synthesizes the results gathered in the 6 workshops held so far. The project could represent a possible point of reference for other countries wishing to undertake similar initiatives.

Impact of hydrogen injection on thermophysical properties and measurement reliability in natural gas networks

In the context of the European decarbonization strategy, hydrogen is a key energy carrier in the medium to long term. The main advantages deriving from a greater penetration of hydrogen into the energy mix consist in its intrinsic characteristics of flexibility and integrability with alternative technologies for the production and consumption of energy. In particular, hydrogen allows to: i) decarbonise end uses, since it is a zero-emission energy carrier and can be produced with processes characterized by the absence of greenhouse gases emissions (e.g. water electrolysis); ii) help to balancing electricity grid supporting the integration of non-programmable renewable energy sources; iii) exploit the natural gas transmission and distribution networks as storage systems in overproduction periods. However, the hydrogen injection into the natural gas infrastructures directly influences thermophysical properties of the gas mixture itself, such as density, calorific value, Wobbe index, speed of sound, etc [1]. The change of the thermophysical properties of gaseous mixture, in turn, directly affects the end use service in terms of efficiency and safety as well as the metrological performance and reliability of the volume and gas quality measurement systems. In this paper, the authors present the results of a study about the impact of hydrogen injection on the properties of the natural gas mixture. In detail, the changes of the thermodynamic properties of the gaseous mixtures with different hydrogen content have been analysed. Moreover, the theoretical effects of the aforementioned variations on the accuracy of the compressibility factor measurement have been also assessed.

Fast frequency regulation and synthetic inertia in a power system with high penetration of renewable energy sources: Optimal design of the required quantities

Abstract Fast primary regulation and synthetic inertia supply are two novel ancillary services for power system frequency control. Although general agreement about their definition is still missing, these services have started to be recognized and inserted into the grid codes of some transmission system operators, especially to handle isolated systems with large penetration of non-programmable renewable energy sources. The main goal here is to propose a methodology to quantify the capacity needed for each service, in order to guarantee system stability and security when the regulation margins of the conventional generation for standard primary frequency control have already been exploited. In terms of control theory, identifying the required service amounts means computing the values of the gains associated to the related controllers. These are found by solving a non-linear optimization problem: the functional to be minimized is related to the maximum value of the power exchanged for each service after an imbalance and to the relative costs of such maximum power exchanges, while constraints are defined to limit the amplitude and the gradient of the response of grid frequency to the imbalance. The optimization procedure has been tested on an annual scenario for the power system of the Sardinia island: more precisely, with reference to predictions for 2030 of the hourly operating conditions of the system, control parameters have been optimized for each hour, by assuming the loss of the maximum generation or of the maximum absorption as the imbalance source. The most critical situation found in the simulations is for the loss of the maximum absorption: a high gain for the synthetic inertia controller and an even higher gain for the fast primary controller are needed to avoid both the curtailment of renewables and load shedding.

A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

A Coordinated Voltage and Reactive Power Control Architecture for Large PV Power Plants

The increasing presence of nonprogrammable renewable energy sources (RES) forces towards the development of new methods for voltage control. In the case of centralized generation, the hierarchical regulation or secondary voltage regulation (SVR) is guaranteed by coordinated voltage and reactive power controls in transmission systems. This type of regulation loses effectiveness when the generation becomes distributed and based on small and medium sized generators. To overcome this problem, it is important that also distributed generators, typically based on RES, participate in the voltage regulation. By starting from the methodologies already applied, this work wants to present a new method for involving distributed generators in SVR. The novelty is given by the application of an existing methodology to the new configuration of electrical grids characterized by a relevant distributed generation. The aim is to control the distributed generators (DGs) as coordinated sources of reactive power for conveniently supporting the voltage regulation. In this paper, a real large photovoltaic (PV) plant is considered. The power plant is composed of several PV generators connected through a distribution network. With the algorithm proposed, the set of generators can be treated as a single traditional power plant that can participate in the hierarchical voltage regulation. The reactive power of each single generator is coordinated in a way similar to the SVR used in several national systems.

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.

Performance Indicators of Electricity Generation at Country Level—The Case of Italy

Power Grids face significant variability in their operation, especially where there are high proportions of non-programmable renewable energy sources constituting the electricity mix. An accurate and up-to-date knowledge of operational data is essential to guaranteeing the optimal management of the network, and this aspect will be even more crucial for the full deployment of Smart Grids. This work presents a data analysis of the electricity production at the country level, by considering some performance indicators based on primary energy consumption, the share of renewable energy sources, and CO2 emissions. The results show a significant variability of the indicators, highlighting the need of an accurate knowledge of operational parameters as a support for future Smart Grid management algorithms based on multi-objective optimization of power generation. The renewable share of electricity production has a positive impact, both on the primary energy factor and on the CO2 emission factor. However, a strong increase of the renewable share requires that the supply/demand mismatching issues be dealt with through appropriate measures.

Improving flexibility of industrial microgrids through thermal storage and HVAC management strategies

The increasing share of non-programmable renewable energy sources in national energy portfolios requires a high flexibility to balance demand and offer in energy markets. Demand side management programs and microgrids will play a key role in achieving flexibility on the demand side. This paper aims at presenting the increase of flexibility that can be achieved by an industrial microgrid. On field tests were carried out in an Italian industrial microgrid, where a set of load management strategies were implemented. These strategies aim at leveraging the thermal inertia of a building using both thermal energy storage and the HVAC system. Results show that the thermal energy storage can contribute to limit the peak cooling load by up to 40 kWe for three hours, while implementing a load shifting strategy using the HVAC system can provide a temporary reduction in power consumption of 20 kWe. Results also prove that it is possible to identify the effect of a load shifting strategy using electricity consumption data sampled with a 15-minutes granularity.

Operational analysis of natural gas combined cycle CHP plants: Energy performance and pollutant emissions

The natural gas-fired combined cycle (NGCC) plants are among the best technologies for power production, especially when operating in combined heat and power (CHP) generation feeding a district heating (DH) network. Even if usually designed to operate with very high utilization factors, thus satisfying mainly the base load, nowadays these plants are often used also as backup power. This is due mainly to the necessity to compensate the non-programmable renewable energy sources (RES) production, and it can be done, thanks to the good flexibility of these plants. However, in off-design conditions, the energy performance and the pollutant emissions may not be as good as the expected nominal ones. In this paper, the real operation of three NGCC units has been analysed in detail by considering mean hourly data over several years. A gas turbine efficiency curve at partial loads has been obtained, showing a decrease of conversion efficiency at lower unit loads. The CO emissions during the start-up and shut-down procedures of the plant reached values that are some orders of magnitude higher than in normal operation. This criticality should not be forgotten when using these units for frequent on-off operations.