Integration Of Renewable Generation Research Articles

An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles

Recent trends in the increasing penetration of renewable energy generation and the increasingly massive integration of electric vehicles (EVs) into multiple-microgrid (MMG) systems pose tremendous challenges to the effective energy management of these systems. However, the solutions of current energy management methods have the disadvantage of excessively limited robustness against uncertain fluctuations in renewable energy generation, and existing hierarchical EV scheduling methods are solved via an iterative process that is prohibitively time-consuming for real-time practical applications. This work addresses the abovementioned issues by constructing an improved data-driven polyhedral uncertainty set based on modified self-organizing feature map neural networks that perform data clustering. In addition, an iteration-free hierarchical framework is proposed for conducting the tri-layer energy management of MMGs with the massive integration of EVs. Specifically, the global positive power factor of EV aggregation is proposed to formulate a set of constraints into the upper-layer model of the tri-layer framework, which can guarantee the feasibility of the middle-and-lower layer models. The value of this factor is determined using a multi-objective optimization algorithm that makes a trade-off between the feasible range of the output of EV aggregators and the degree to which the trip demands of EVs are violated. The superiority and effectiveness of the proposed iteration-free robust hierarchical energy management method for MMGs are verified via simulations involving a practical MMG under high penetrations of renewable energy generation and massive integration of EVs.

Tool for evaluation of energy system options for municipalities

To reach the goal of net-zero emissions in 2050, posed by the Swiss energy strategy, our society faces major challenges for the integration of renewable energy generation and distribution, and changes in the energy system are inevitable. The development of decentralised renewable energy sources and the necessary extensions of the power grid are only one aspect. A high percentage of the energy consumption in the residential sector consists of space heating and cooling of buildings. Therefore, the changes will also affect the thermal energy sector, e.g. through renovations of buildings, replacements of fossil heating systems with heat pumps or connections to district heating networks. To support the planning involved in the changes of the energy system, we provide a tool for visualisations of energy system options and scenarios and recommendations for optimisations based on publicly available data, modelling demands and simulating energy systems in a mixed-integer linear programming framework. We present a first version of the recommender tool which focuses on the support of municipality representatives for drawing up their communal energy strategies.

Optimal Grid Flexibility Assessment for Integration of Variable Renewable-Based Electricity Generation

This study delves into power system flexibility, with a keen focus on the integration of variable renewable electricity generation into power grids. Two scenarios were analyzed. The base scenario revealed an aging grid, insufficient generation capacity, frequent outages, and little renewable energy generation (1.9%), along with a significant (71.23%) loss of load. In contrast, the investment scenario presented solutions including raising VRE capacity to 44%, adding 1000 MW capacity transmission lines, installing 200 MW capacity grid-scale battery storage, and technological enhancements. These interventions effectively eliminated loss of load, reinforcing energy resilience. Investments in CCGPP and grid-scale batteries proved instrumental in mitigating the variability of renewable energy. Improved transmission promised efficient power exchange and regional collaboration. The elimination of annualized energy spills and the removal of ramping constraints marked significant strides in enhancing power system flexibility. This research underscores the pivotal role of grid flexibility in accommodating VRE sources. By implementing the proposed optimal solutions, Afghanistan can lead the way toward a cleaner, more resilient, and more interconnected energy future. These findings offer a replicable framework for addressing similar challenges in integrating renewable energy sources globally and supporting the transition to sustainable and reliable energy.

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.

Distributionally robust hierarchical multi-objective voltage risk management for unbalanced distribution systems

The large-scale integration of renewable energy generators (REGs) is key to reducing carbon emissions. However, the intermittency of REG outputs poses operational risk challenges on distribution systems (DSs). This paper proposes a novel Wasserstein-based distributionally robust assessment for the voltage risk index that represents both likelihood and non-linearly increasing severity of voltage violations. We propose a hierarchical DRO model for risk management that integrates a multi-objective distributionally robust optimization (DRO) at the distribution system operator (DSO) level and a basic DRO at the user level. Then, a systematic solution method is developed, where the proposed model is equivalently reformulated into tractable bilevel mixed-integer convex programs. The effectiveness, advantages, and scalability of the proposed method are validated on the IEEE 33-bus system and unbalanced IEEE-123-bus system.

The low-carbon economic operation strategy of virtual power plant under different electricity-gas-heat-carbon multi-market synergy scenarios

Studying the grid integration of renewable energy power generation is crucial for achieving the goal of carbon neutrality since it may have a significant influence on the secure and reliable functioning of the power system. In order to solve the problem of deviation impact caused by renewable energy fluctuations and the optimal scheduling of VPP (Virtual Power Plant), the study divides the internal aggregation unit of the virtual power plant into two parts to model. One part is the source equipment, including wind power generation equipment, gas turbine, gas boiler and waste heat boiler. And the other part is the generalized Energy storage, including electric vehicles, air conditioners and alternative response loads. Ultimately, a generalized energy storage-based virtual power plant operation optimization model is developed under multi-market coordination of electricity-gas-heat-carbon. According to the study’s findings, adding more power-to-gas technology boosts revenue in the carbon trading market by 25.24 percent. The energy market’s revenue is equal to that in the absence of a carbon trading market, and the income of the natural gas market increases by $ 32.96. The income of the carbon trading market is $ 181.51, and the final operating cost is reduced by $ 180.80, a drop of 7.81%. To sum up, the suggested approach may more effectively achieve the best distribution of different energy sources, increase the dependability of VPP operation, and make it more low-carbon.

The VSC-HVDC Transmission System Performance Assessment

As the use of renewable resources increases the transfer of power from remote area to consumer is a big challenge. Therefore, Voltage Source Converter High Voltage Direct Current (VSC-HVDC) transmission is gaining more popularity for integration of renewable energy generators to the consumers using smart grid. Even after many advantages of HVDC transmission system it is not used popularly due to unexpected instability in the system. However recent developments in semiconductor devices and control techniques the HVDC link operation in transmission system becomes easier. In this paper VSC based HVDC system is designed with AC-DC converter model for power flow assessment of both sides of HVDC link. To analyze the proposed model a simulation model of 75km DC line with voltage source converter (VSC) based AC-DC converter model on both side of DC line is designed in a MATLAB environment. HVDC link efficiency is calculated by analyzing power flow through AC lines at both ends of HVDC line.

Quantification of flexibility from the thermal mass of residential buildings in England and Wales

The increased integration of variable renewable generation into the power systems, along with the phase-out of fossil-based power stations, necessitate procuring more flexibility from the demand sectors. The electrification of the residential heat sector is an option to decarbonise the heat sector in the United Kingdom. The inherent flexibility that is available in the residential heat sector, in the form of the thermal inertia of buildings, is expected to play an important role in supporting the critical task of short-term balancing of electricity supply and demand. This paper proposes a method for characterising the locally aggregated flexibility envelope from the electrified residential heat sector, considering the most influential factors including outdoor and indoor temperature, thermal mass and heat loss of dwellings. Applying the method to England and Wales as a case study, demonstrated a significant potential for a temporary reduction of electricity demand for heating even during cold days. For a scenario envisaged a fully electrified residential heat sector in England and Wales, total electricity demand reductions of approximately 25 GW and 85 GW were shown to be achievable for the outdoor temperature of 10 °C and -5 °C, respectively. Improving the energy performance of the housing stock in England and Wales was shown to reduce the magnitude of available flexibility to approximately 18 GW and 60 GW for the outdoor temperature of 10 °C and -5 °C, respectively. This is due to the use of smaller size heat pumps in the more efficient housing stock. However, the impact of the buildings' retrofit on their thermal mass and consequently on the duration of the flexibility provision is uncertain.

Determining optimal generator start-up sequence in bulk power system restoration considering uncertainties: A confidence gap decision theory based robust optimization approach

A reasonable start-up sequence of generators considering renewable energy generation integration can effectively improve the restoration efficiency of the power system concerned. The restoration strategy of generators is time-sensitive, however, the computation procedure of existing methods is not fast enough to meet the requirements of practical applications. In this paper, the generator start-up sequence optimization (GSSO) problem is addressed with the integration of wind-solar hybrid systems (WSSs) considered in bulk power systems. Specifically, a deterministic model for GSSO is formulated to maximize the total generation capability during the restoration procedure, in which fewer auxiliary variables are required to promote the computational efficiency. On this basis, in order to deal with the inherent and significant uncertainties of WSSs, a confidence gap decision theory (CGDT) based approach is proposed combining the advantages of stochastic optimization and the information gap decision theory based method while considering the worst-case realization of unknown probability distributions, so that a chance-constrained robust generator start-up solution is thereby attained. Moreover, the formulated CGDT based model is transformed into a mixed integer linear programming problem. Finally, the IEEE 39-bus power system and a modified version of an actual power system in China are employed to demonstrate the feasibility and efficiency of the proposed method.

A VAE-Bayesian deep learning scheme for solar power generation forecasting based on dimensionality reduction

The advancements in distributed generation (DG) technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems. However, the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties. This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory (BiLSTM) neural networks, while handling the high dimensionality in weight parameters using variational auto-encoders (VAE). The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error (RMSE), Pinball loss, etc. Furthermore, reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component. When compared with benchmark methods, the proposed method leads to significant improvements in weight reduction, i.e., from 76,4224 to 2,022 number of weight parameters, quantifying to 97.35% improvement in weight parameters reduction and 37.93% improvement in computational time for 6 months of solar power generation data.

Merchant and regulated storage investment in energy and reserve markets: A Stackelberg game

AbstractWith large‐scale integration of renewable generation, energy storage is expected to play an important role in providing flexibility to energy systems. In this paper, the authors construct a trilevel Stackelberg game model to study the co‐investment of merchant and regulated storage in energy and reserve markets. The upper‐level problem is a profit‐maximizing storage investment problem with a desired rate‐of‐return solved by a merchant investor. In the middle‐level problem, the system operator (SO) makes regulated storage investment decisions to minimize system cost. In the lower‐level problem, the SO clears energy and reserve markets. The proposed model captures interactions of regulated and merchant storage investment. Also, it clarifies how different ownership structures of storage influence merchant storage profitability and system cost structures in different capital cost of storage investment and wind penetration level scenarios. The numerical results conducted on a 6‐bus illustrative example and the IEEE 24‐bus Reliability test case validate the proposed model. The results show that both regulated and merchant storage can increase social welfare, and social welfare remains almost the same under different ownership structures of storage.

Voltage profile improvement and power loss minimization of new haven injection substation for optimal placement of DG using harmony search algorithm optimization technique

The main importance of a power grid is to transfer electrical energy from the production to the consumption, while maintaining an acceptable reliability and voltage quality for all customers. The role of electrical power distributions to have centralized plants distributing electricity through Distributed Generation (DG) which reduces the Power Loss on transmission and distribution station and also improve voltage profile by optimization. This research work analyzes New Haven injection substation for optimal placement of DG generation based on wind power renewable energy source to the Distribution network and voltage stability using Harmony Search Algorithm. The model developed was simulated using MATLAB/SIMULINK software. The work applied new improved Harmony Search Algorithm for integration of renewable energy generation in the distribution network with positive impact on the operation of the voltage stability between 0.95 to 1.05p.u and reactive power distribution improvement through minimization of line losses. The total power system Loss without and with DG are 338.712KW and 307.9KW. Also, the voltage profiles of the sixteen faulty buses are stabilized for efficient performance of substation. Hence, the percentage of power system improvement is 27.23%. Therefore from the results, there is reduction of power Loss when DG is applied and optimised in the power system.

Modeling demand flexibility impact on the long-term adequacy of generation systems

This paper proposes a novel probabilistic model for quantifying the impact of demand flexibility (DF) on the long-term generation system adequacy via Sequential Monte Carlo Simulation (SMCS) method. Unlike load shedding, DF can be considered an important instrument to postpone bulk consumption from periods with limited reserves to periods with more generating capacity available, avoiding load shedding and increasing the integration of variable renewable generation, such as wind power. DF has been widely studied in terms of its contribution to the system’s social welfare, resulting in numerous innovative approaches ranging from the flexibility modeling of individual electric loads to the definition of aggregation strategies for optimally deploying this lever in competitive markets. To add to the current state-of-the-art, a new model is proposed to quantify DF impact on the traditional reliability indices, such as the Loss of Load Expectation (LOLE) and the Expected Energy Not Supplied (EENS), enabling a new perspective for the DF value. Given the diverse mechanisms associated with DF of different consumer types, the model considers the uncertainties associated with the demand flexibility available in each hour of the year and with the rebound effect, i.e., the subsequent change of consumption patterns following a DF mobilization event. Case studies based on a configuration of the IEEE-RTS 79 test system with wind power demonstrate that the DF can substantially improve the reliability indices of the static and operational reserve while decreasing the curtailment of variable generation cause by unit scheduling priorities or by short-term generation/demand imbalances.

A two-stage coordinated capacity expansion planning model considering optimal portfolios of flexibility resources

Renewable energy sources, such as wind and solar photovoltaic, have been widely deployed in power systems due to the decarbonization transition target and technological advances. The large-share integration of renewable generation contributes to the construction of a low-carbon power system and imposes significant flexibility requirements for the power system. The reliability and security in power system operation and RES generation accommodation are deeply involved with the configuration mixes and the portfolio of flexible resources. Therefore, this paper proposes a coordinated capacity expansion planning model with a variety of flexibility technologies, including thermal power flexibility retrofitting, energy storage systems, demand-side responses, as well as concentrating solar power plants. A scenario-based approach is adopted to capture uncertainties from electric load and renewable generation. The proposed planning model is constructed as a two-stage stochastic planning framework, where the first stage optimizes investment strategies of flexible technologies while the second stage determines the optimal power dispatch for all generation assets under different operation conditions. The hourly operation simulation model is also embedded in the proposed planning model with detailed operation constraints. The effectivity of the proposed coordinated planning model is verified by a modified reliability and operation test system. Numerical results show that different combinations of flexibility technologies have distinct impacts on the power system’s economic and environmental performances, which implies the significance of finding an optimal portfolio of flexibility resources in the power system with large-scale renewable energy sources.

Power System Flexibility Enhancement with Energy Storages Considering Charging Characteristics

With the increasing integration of renewable energy generations, the renewables’ uncertainties impact the power system operation. So, it is critical to enhancing the system’s operational flexibility. To this end, one of the most useful solutions is to deploy energy storage. This paper proposes a power system flexibility enhancement method using energy storage systems (ESSs). The grid’s flexibility requirement is analyzed, and the flexibility enhancement model is constructed, considering the ESSs’ charging and discharging characteristics. Case studies on an IEEE 33-node system verify the quantitative method. The conclusions indicate the ESSs could efficiently meet the system’s flexibility requirements.

Risk-averse optimal participation of a DR-intensive microgrid in competitive clusters considering response fatigue

The massive integration of renewable generators, energy storage systems, and demand response requires the development of smart power infrastructures. In this upcoming context, microgrids will be essential for the optimal integration of such assets. When different microgrids are located near each other, they can be centrally coordinated within a novel paradigm called Microgrid Cluster. In such structures, the microgrids involved can collaborate in a cooperative way or compete by developing internal market structures. This paper develops a novel optimal bidding strategy for a demand response-intensive microgrid partaking in competitive clusters. The new proposal is envisaged as a three-stage methodology that aims at reducing the effects of response fatigue. Uncertainties related to inflexible demand and renewable generation are modeled via scenarios, while the risk associated with uncertain parameters is handled by enforcing the Conditional Value at Risk. The resulting computational tool is effective and tractable, as shown in the results obtained on a benchmark three-microgrids cluster. Indeed, the developed methodology is able to reduce the total response signals by 88 % in some cases. Moreover, this case study allows analyzing the effect of response fatigue minimization in the overall cluster performance, showing that the collective welfare can be reduced by 32 % when response fatigue is taken into account.

Evaluation of residential demand response trials with smart heat pumps and batteries and their effect at the substation feeder

Residential demand response using low carbon technologies can potentially offer energy flexibility to the electricity network along with integration of renewable generation. This paper seeks to empirically evaluate the effectiveness of residential demand response trials on a low voltage feeder of a secondary substation in Barnsley (UK). The study used a sample of 14 well-insulated dwellings equipped with home batteries, heat pumps and solar photovoltaic systems coordinated using automated control. Statistical analysis was undertaken using time-series monitoring data obtained at the individual dwelling level, dwelling sample and at the feeder level of the local low voltage network. Resident experience of the trials was assessed through qualitative data obtained from household telephone surveys. Over three weeks of trials, daily demand response interventions of 2 h duration were applied to the sample of 14 dwellings. For evening peak times, the mean reduction in grid electricity import was found to be 1.3 kWh (67%) per dwelling for turn-down interventions which aimed to minimise import. For turn-up interventions between 1 and 3 pm, the mean increase in grid electricity import was found to be 5.8 kWh (645%) per dwelling. The effect of interventions was measured at the low voltage network level for two single-phase feeders, where penetration of trial homes was approximately one-third. A reduction in mean real power up to 21% was observed for turn-down interventions as well as an increase in real mean power up to 307% for turn-up interventions. In general, the trials had little effect on residents in terms of thermal comfort, hot water availability, noise disturbance or disruption to routines, and where such effects were noticed, they were broadly acceptable. The widespread implementation of residential demand response schemes will require increased roll-out of time-of-use tariffs, enhanced resident support and extensive monitoring of low voltage feeders in electricity substations.

Electron and Ion Transport in Lithium and Lithium-Ion Battery Negative and Positive Composite Electrodes.

Electrochemical energy storage systems, specifically lithium and lithium-ion batteries, are ubiquitous in contemporary society with the widespread deployment of portable electronic devices. Emerging storage applications such as integration of renewable energy generation and expanded adoption of electric vehicles present an array of functional demands. Critical to battery function are electron and ion transport as they determine the energy output of the battery under application conditions and what portion of the total energy contained in the battery can be utilized. This review considers electron and ion transport processes for active materials as well as positive and negative composite electrodes. Length and time scales over many orders of magnitude are relevant ranging from atomic arrangements of materials and short times for electron conduction to large format batteries and many years of operation. Characterization over this diversity of scales demands multiple methods to obtain a complete view of the transport processes involved. In addition, we offer a perspective on strategies for enabling rational design of electrodes, the role of continuum modeling, and the fundamental science needed for continued advancement of electrochemical energy storage systems with improved energy density, power, and lifetime.

Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Algorithm

In the last few years, the integration of renewable distributed generation (RDG) in the electrical distribution network (EDN) has become a favorable solution that guarantees and keeps a satisfying balance between electrical production and consumption of energy. In this work, various metaheuristic algorithms were implemented to perform the validation of their efficiency in delivering the optimal allocation of both RDGs based on multiple photovoltaic distributed generation (PVDG) and wind turbine distributed generation (WTDG) to the EDN while considering the uncertainties of their electrical energy output as well as the load demand’s variation during all the year’s seasons. The convergence characteristics and the results reveal that the marine predator algorithm was effectively the quickest and best technique to attain the best solutions after a small number of iterations compared to the rest of the utilized algorithms, including particle swarm optimization, the whale optimization algorithm, moth flame optimizer algorithms, and the slime mold algorithm. Meanwhile, as an example, the marine predator algorithm minimized the seasonal active losses down to 56.56% and 56.09% for both applied networks of IEEE 33 and 69-bus, respectively. To reach those results, a multi-objective function (MOF) was developed to simultaneously minimize the technical indices of the total active power loss index (APLI) and reactive power loss index (RPLI), voltage deviation index (VDI), operating time index (OTI), and coordination time interval index (CTII) of overcurrent relay in the test system EDNs, in order to approach the practical case, in which there are too many parameters to be optimized, considering different constraints, during the uncertain time and variable data of load and energy production.

Active Damping Stabilization Techniques for Cascaded Systems in DC Microgrids: A Comprehensive Review

Microgrids have arisen as an alternate solution to the problem of power generation in distributed energy. Compared to ac microgrids, dc microgrids (DC MGs) are superior in terms of system efficiency, power quality, affordability, and ease of control. For the integration of renewable energy generation into microgrids, power electronic converters are required. When power electronics converters are tightly regulated, they behave as a constant power load (CPL) which exhibits a negative incremental impedance characteristic. As a result, oscillations occur in voltage response at a DC bus. To suppress the voltage oscillations in DC MGs, various damping stabilization techniques are proposed by researchers. This paper provides a comprehensive review on active damping stabilization techniques. To improve the system’s stability, active damping can be implemented in three distinct zones: source-side, load side or CPL side, and intermediate level. After analyzing each zone, their merits and drawbacks have been presented. Moreover, CPL modelling, performance of cascaded DC-DC systems, the effect of the load on the source subsystem, the effect of the source on the load converter, and stability analysis are discussed. Finally, concluding remarks and future research directions are highlighted.