Volatile Renewable Generation Research Articles

System friendliness of distributed resources in sustainable energy systems

The increasing share of volatile renewable energy generation in current and future energy systems which does not follow the demand, makes it necessary to shift the synchronization tasks to the system and demand level. The intended demand side consumption management is usually incentivized via financial mechanisms. However, the optimum design of related incentives is still unclear. It is usually discussed under the term “system friendliness” which in turn still lacks a broadly accepted definition. In this article we introduce a new technical definition of “system friendliness” and develop comprehensive and holistic related indicators. For that, we introduce the burden concept for energy systems which represents the amount of technical infrastructure required for stable operation. We use the concept of a hypothetical system energy storage to derive indicators that are capable of quantifying the system friendliness of a given point-of-interest. The developed indicators are independent of regulations or market environments which enables comparability of results between studies and different systems. In a case study we demonstrate our newly developed methodology and exemplary examine decentral district energy storages. Our findings demonstrate that today’s regulatory environments that usually support residential self-consumption maximization are counterproductive. Instead, we show that dynamic end-user prices coupled with dynamic feed in tariffs are able to incentivize an almost 100% system friendly behavior of distributed prosumers.

Potential of hydrogen and thermal storage in the long-term transition of the power sector: A case study of China

Hydrogen and thermal storage can reduce cost of long-term and large-scale energy storage with high efficiency and low or even zero carbon emissions. Their potential in the low-carbon transition pathway of an energy system with rapid growth of energy demand, large shifting of energy supply structure and limited investment budget remains unclear. A long-term power generation planning model is proposed in this paper, featuring detailed technical and economic characteristics of hydrogen and thermal storage. The power supply system of China is selected as a case study, due to its urgent need for low-carbon transition and complex spatial characteristics. Results show that the application of hydrogen and thermal storage can benefit the development of volatile renewable power generation technologies, facilitate the transition towards zero or even negative carbon emissions while simultaneously reducing power supply costs. Hydrogen is expected to be mainly produced in spring and consumed in summer while heat is expected to be primarily generated in autumn and consumed in winter. Shifting from thermal storage to hydrogen storage might happen around 2050 in accordance to the low-carbon transition progress.

Quantifying the Operational Flexibility of Distributed Cross-Sectoral Energy Systems for the Integration of Volatile Renewable Electricity Generation

As a part of the transition in higher-level energy systems, distributed cross-sectoral energy systems (DCESs) play a crucial role in providing flexibility in covering residual load (RL). However, there is currently no method available to quantify the potential flexibility of DCESs in covering RL. This study aimed to address this gap by comparing the RL demand of a higher-level energy system with the electricity flow between a DCES and the electricity grid. This can allow for the quantification of the flexibility of DCES operation. Our approach was to categorize existing methods for flexibility quantification and then propose a new method to assess the flexibility of DCESs in covering RL. For this, we introduced a new quantification indicator called the Flexibility Deployment Index (FDI), which integrates two factors: the RL of the higher-level energy system and the electricity purchase and feed-in of a DCES. By normalizing both factors, we could compare the flexibility to cover RL with respect to different DCES concepts and scenarios. To validate the developed quantification method, we applied it to a case study of a hospital’s DCES in Germany. Using an MILP optimization model, we analyzed the variation in FDI for different technology concepts and scenarios, including fixed electricity tariffs, dynamic electricity tariffs, and CO2-emission-optimized operation. The results of our calculations and the application of the FDI indicate that high-capacity combined heat and power units combined with thermal storage units provide higher flexibility. Additionally, the results highlight higher flexibility provision during the winter period compared to the summer period. However, further application and research are needed to confirm the robustness and validity of the FDI assessment. Nonetheless, the case study demonstrates the potential of the new quantification method.

Risk‐based stochastic scheduling of centralised and distributed energy storage systems

AbstractThe authors propose a continuous‐time two‐stage stochastic optimisation model for the integration of centralised and distributed energy storage (ES) systems into power systems with high levels of volatile renewable generation. In the proposed model, centralised and distributed ES systems, respectively, controlled by the independent system operator and distribution system operator, can offer energy and flexibility to the operation of the power system in order to help accommodate the uncertainties of renewable generation and load. The proposed model considers the power network constraints and minimises the day‐ahead and real‐time operation costs of the system in the first and second stages. The proposed model utilises energy and ramping flexibility trajectories of ES systems to enhance the cost‐effective operation of the power system, countering the financial risks imposed by the integration of uncertain load and renewable generation. Simulations are conducted on the IEEE 24‐bus reliability test system with multiple risk‐aversion levels for the power system operator, and the results demonstrate the efficiency of the proposed model in utilising the ES systems to provide energy and ramping flexibility and reducing the financial risks measured by the conditional value at risk.

On data-driven modeling and control in modern power grids stability: Survey and perspective

Modern power grids are fast evolving with the increasing volatile renewable generation, distributed energy resources (DERs) and time-varying operating conditions. The DERs include rooftop photovoltaic (PV), small wind turbines, energy storages, flexible loads, electric vehicles (EVs), etc. The grid control is confronted with low inertia, uncertainty and nonlinearity that challenge the operation security, efficacy and efficiency. The ongoing digitization of power grids provides opportunities to address the challenges with data-driven and control. This paper provides a comprehensive review of emerging data-driven dynamical modeling and control methods and their various applications in power grid. Future trends are also discussed based on advances in data-driven control.

Storage size estimation for volatile renewable power generation: an application of the Fokker–Planck equation

Storage size estimation for volatile renewable power generation: an application of the Fokker–Planck equation

Generation of optimized schedules of a charging process under the application of dynamic electricity tariffs and self-consumption optimization

This paper describes a way to implement an optimized operational behavior for charging processes in the field of e-mobility by automatically generating load profiles. The presented concept aims at adapting the power supply to the volatile renewable energy generation in the power system. By automatically adjusting load profiles, electricity customers will optimize their electricity consumption in the future to promote the integration of renewable energies and minimize their electricity procurement costs. In addition, the power grids are relieved locally by realizing a synchronization of the load profile with the customer's power generation. In this context, a concept for applying self-consumption optimization and price differences through a dynamic electricity tariff is provided for the electricity customer. The present work deals with a data model relevant for automated control based on the abovementioned criteria. Based on an annual simulation of the customer's economic potential, it is shown that the optimized mode of operation can significantly reduce the electricity procurement costs of a charging process.

Potential of a self-sufficient control for flexible loads in low voltage grids

Due to the expansion of volatile renewable generation and the growth in the field of electric vehicles, German distribution system operators are facing new challenges. Adequate infrastructure is needed mitigating the negative impact on the power system stability. Along with these new grid actuators, consumption related capacity-bottlenecks increase. An approach is to affect the consumption behaviour. In order to replace rigid technologies such as time switches, a grid-serving load management concept is being pursued. The approach presented in this paper is based on a decentralized, self-sufficient control of flexible loads depending on the local grid status. The technological, regulatory and contractual conditions of the flexible loads in the low voltage grid, such as electric vehicles and others, are taken into account. An algorithm for decentralized control based on locally collected measurement data was developed and implemented. This approach is already being tested in a real laboratory on about hundred households in both rural and urban regions. First data from the real laboratory show a high potential of shifting and optimizing the energy consumption of flexible loads over time.

Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization

Thermal energy storage (TES) for a cooling plant is a crucial resource for load flexibility. Traditionally, simple, heuristic control approaches, such as the storage priority control which charges TES during the nighttime and discharges during the daytime, have been widely used in practice, and shown reasonable performance in the past benefiting both the grid and the end-users such as buildings and district energy systems. However, the increasing penetration of renewables changes the situation, exposing the grid to a growing duck curve, which encourages the consumption of more energy in the daytime, and volatile renewable generation which requires dynamic planning. The growing pressure of diminishing greenhouse gas emissions also increases the complexity of cooling TES plant operations as different control strategies may apply to optimize operations for energy cost or carbon emissions. This paper presents a model predictive control (MPC), site demonstration and evaluation results of optimal operation of a chiller plant, TES and behind-meter photovoltaics for a campus-level district cooling system. The MPC was formulated as a mixed-integer linear program for better numerical and control properties. Compared with baseline rule-based controls, the MPC results show reductions of the excess PV power by around 25%, of the greenhouse gas emission by 10%, and of peak electricity demand by 10%.

A coupled transient gas flow calculation with a simultaneous calorific-value-gradient improved hydrogen tracking

Gas systems can provide considerable flexibility in integrated energy systems to accommodate hydrogen produced from Power-to-Hydrogen units using excess volatile renewable energy generation. To use the flexibility in integrated energy systems while ensuring a secure and reliable system operation, gas system operators need to accurately and easily analyze the effects of varying hydrogen levels on the dynamic gas behavior and vice versa. Existing methods for hydrogen tracking, however, either solve the hydrogen propagation and dynamic gas behavior separately or must cope with a large inaccuracy. Hence, existing methods do not allow an accurate and coupled analysis of gas systems in integrated energy systems considering varying hydrogen levels. This paper proposes a calorific-value-gradient method, which can accurately track the propagation of varying hydrogen levels in a gas system even with large simulation time increments of up to one hour. The new method is joined and simultaneously solved with an implicit finite difference scheme describing the transient gas behavior in a single equation system in a coupled Newton–Raphson gas flow calculation. As larger simulation time increments can be chosen without reducing the accuracy, the computation time can be strongly reduced compared to existing Euler-based methods. With its high accuracy and its coupled approach, this paper provides gas system operators a method to accurately analyze how the propagation of hydrogen affects the entire gas system. With its coupled approach, the presented method can enhance the investigation of integrated energy systems as the transient gas behavior and varying hydrogen propagation of the gas system can be easily included in such analyses.

Data-Driven Dispatchable Regions With Potentially Active Boundaries for Renewable Power Generation: Concept and Construction

The dispatchable region of volatile renewable power generation (RPG) quantifies how much uncertainty the power system can handle at a given operating point. State-of-the-art dispatchable region (DR) research has studied how system operational constraints influence the DR but has seldom considered the effect of the uncertainty features of RPG outputs. The traditional DR is generally described by a large number of boundaries, and it is computationally intensive to construct. To bridge these gaps, a novel type of DR is defined, which is enclosed by potentially active boundaries (PABs) that consider the operational constraints and uncertainty features of RPG outputs. The proposed DR is easier to construct because the PABs are only a small part of the traditional DR boundaries. The procedure for constructing the proposed DR is described in terms of the progressive search for PABs, which is formulated as a mixed-integer linear program by incorporating the discrete observed data points of RPG outputs as an approximate distribution. A parallel solution paradigm is also developed to expedite the construction procedure when using a large observed dataset. Simulation tests on the IEEE 30-bus and 118-bus systems verify the effectiveness and scalability of the proposed DR and the efficiency of the proposed algorithm.

Review of energy sharing: Business models, mechanisms, and prospects

The development of distributed generation technology is endowing consumers the ability to produce energy and transforming them into “prosumers”. This transformation shall improve energy efficiency and pave the way to a low-carbon future. However, it also exerts critical challenges on system operations, such as the wasted backups for volatile renewable generation and the difficulty to predict behaviour of prosumers with conflicting interests and privacy concerns. An emerging business model to tackle these challenges is energy sharing, whose concepts, structures, applications, models, and designs are thoroughly reviewed in this paper, with an outlook of future research to better realise its potentials.

A Joined Quasi-Steady-State Power Flow Calculation for Integrated Energy Systems

The network storage capability of district heating systems and gas systems can provide a considerable flexibility in integrated energy systems, increasing the use of volatile renewable energy generation. But the stronger the single energy systems are linked the more complex their operation becomes due to their increased interactions. To ensure a secure and reliable system operation while using the full potential of integrated energy systems, these interactions must be analyzed. Existing power flow calculation methods, however, assume a steady-state behavior of all energy systems in the integrated energy system, neglecting the network storage capability. Hence, this paper presents a joined quasi-steady-state power flow calculation method for integrated energy systems. Our method introduces the dynamic behavior of the district heating system arising from the temperature propagation and the dynamic behavior of the gas system due to the gas compressibility and propagation of hydrogen. In a comparison with a steady-state power flow calculation we show the considerable effect of the network storage on the operation of an integrated energy system. As our method enhances existing steady-state power flow calculation methods, it can be easily used for the same use cases but allowing the full potential of integrated energy systems to be investigated.

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.

Comparison of short-term electrical load forecasting methods for different building types

The transformation of the energy system towards volatile renewable generation, increases the need to manage decentralized flexibilities more efficiently. For this, precise forecasting of uncontrollable electrical load is key. Although there is an abundance of studies presenting innovative individual methods for load forecasting, comprehensive comparisons of popular methods are hard to come across.In this paper, eight methods for day-ahead forecasts of supermarket, school and residential electrical load on the level of individual buildings are compared. The compared algorithms came from machine learning and statistics and a median ensemble combining the individual forecasts was used.In our examination, nearly all the studied methods improved forecasting accuracy compared to the naïve seasonal benchmark approach. The forecast error could be reduced by up to 35% compared to the benchmark. From the individual methods, the neural networks achieved the best results for the school and supermarket buildings, whereas the k-nearest-neighbor regression had the lowest forecasting error for households. The median ensemble narrowly yielded a lower forecast error than all individual methods for the residential and school category and was only outperformed by a neural network for the supermarket data. However, this slight increase in performance came at the cost of a significantly increased computation time. Overall, identifying a single best method remains a challenge specific to the forecasting task.

Large fluctuations in locational marginal prices.

This paper investigates large fluctuations of locational marginal prices (LMPs) in wholesale energy markets caused by volatile renewable generation profiles. Specifically, we study events of the form [Formula: see text] where LMP is the vector of LMPs at the n power grid nodes, and α-, [Formula: see text] are vectors of price thresholds specifying undesirable price occurrences. By exploiting the structure of the supply-demand matching mechanism in power grids, we look at LMPs as deterministic piecewise affine, possibly discontinuous functions of the stochastic input process, modelling uncontrollable renewable generation. We use techniques from large deviations theory to identify the most likely ways for extreme price spikes to happen, and to rank the nodes of the power grid in terms of their likelihood of experiencing a price spike. Our results are derived in the case of Gaussian fluctuations, and are validated numerically on the IEEE 14-bus test case. This article is part of the theme issue 'The mathematics of energy systems'.

Robust Operational Equilibrium for Electricity and Gas Markets Considering Bilateral Energy and Reserve Contracts

The increasing integration of uncertain and volatile renewable power generation poses challenges to the operation of the interdependent electricity and gas markets. In this paper, a robust operational equilibrium solution method for the interactive markets of power and gas systems is proposed, where the bidirectional interactions include both firm energy and reserve contracting, and the impacts of the uncertainties of wind generation outputs on the two markets are characterized. The line pack as well as bidirectional flow characteristics of gas pipelines are depicted in the gas system model so as to improve its operational flexibility. To guarantee the robustness of market equilibrium against uncertainties, the power and gas market clearing models become two-stage robust ones. Column-and-constraint generation (C&CG) based and the best response algorithms are devised to clear the two markets as well as to coordinate them, respectively. Simulation results validate the effectiveness of the proposed robust operational equilibrium model and the performance of the devised solution methodology.

Comparative analysis of renewable energy integration in Germany and Russia

The problems and prospects of renewable energy penetration to the Russian regional energy systems are discussed. The comparative analysis of renewable technologies development was done considering Russian and Germany power systems as ones with quite similar initial conditions before the energy transition has been started. Implementation a considerable renewable generation share into a “traditional” power system seems to be perfectly technically feasible still with the state-of-the-art technologies. Substantial organizational, financial and economical efforts seem to be unavoidable. Careful preliminary planing of renewables implementation is crucial to ensure viability of the volatile renewable generation working as a part of the Russian power system.

Optimal Power Flow Management System for a Power Network with Stochastic Renewable Energy Resources Using Golden Ratio Optimization Method

An optimal operation system is a potential solution to increase the energy efficiency of a power network equipped with stochastic Renewable Energy Sources (RES). In this article, an Optimal Power Flow (OPF) problem has been formulated as a single and multi-objective problems for a conventional power generation and renewable sources connected to a power network. The objective functions reflect the minimization of fuel cost, gas emission, power loss, voltage deviation and improving the system stability. Considering the volatile renewable generation behaviour and uncertainty in the power prediction of wind and solar power output as a nonlinear optimization problem, this paper uses a Weibull and lognormal probability distribution functions to estimate the power output of renewable generation. Then, a new Golden Ratio Optimization Method (GROM) algorithm has been developed to solve the OPF problem for a power network incorporating with stochastic RES. The proposed GROM algorithm aims to improve the reliability, environmental and energy performance of the power network system (IEEE 30-bus system). Three different scenarios, using different RES locations, are presented and the results of the proposed GROM algorithm is compared to six heuristic search methods from the literature. The comparisons indicate that the GROM algorithm successfully reduce fuel costs, gas emission and improve the voltage stability and outperforms each of the presented six heuristic search methods.

Facilitating renewables and power-to-gas via integrated electrical power-gas system scheduling

The possibly increasing volatile gas off-take from gas-fired power plants to accommodate volatile renewable generation in combination with the integration of power-to-gas (P2G) warrants further study into the operation of a coupled electrical power and natural gas system. Therefore, this paper presents and validates a novel operational model comprising both the electrical power and gas systems. Model improvements include (i) the use of zonal gas loads in addition to nodal loads, (ii) ramp rates for conventional gas production facilities and, (iii) an improved detailed technological model of P2G units, which all increase the realism of the obtained results. Results of several small-scale case studies illustrate the relevance of these model additions. In addition, a case study inspired by the Belgian electrical power and gas systems shows that the Belgian gas network has abundant capacity to integrate a possibly volatile injection of synthetic methane from P2G. This model may be used by electricity and gas transmission system operators to study the interaction between their systems and inform policy makers and regulators.