Incorporating Energy Storage Research Articles

Hydropower station scheduling with ship arrival prediction and energy storage

Effectiveness improvement in power generation and navigation for grid-connected hydropower stations have emerged as a significant concern due to the challenges such as discrepancies between declared and actual ship arrival times, as well as unstable power generation. To address these issues, this paper proposes a multi-objective real-time scheduling model. The proposed model incorporates energy storage and ship arrival prediction. An energy storage mechanism is introduced to stabilize power generation by charging the power storage equipment during surplus generation and discharging it during periods of insufficient generation at the hydropower stations. To facilitate the scheduling with the eneragy storage mechanism, the arrival time of ships to the stations are predicted. We use the maximization of generation minus grid load demand and the maximization of navigability assurance rate as two objective functions in the scheduling process. The model uses the Non-Dominated Sorting Beluga Whale Optimization (NSBWO) algorithm to optimize and solve the real-time discharge flow scheduling of the hydropower stations in different time periods. The NSBWO algorithm combines the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Beluga Whale Optimization (BWO). The experimental results show that the proposed method has advantages in predicting the expected arrival time of ships and scheduling the discharge flow. The prediction using XGBoost model reaches accuracy with more than 0.9, and the discharged flow obtained from scheduling meets the demand of hydropower stations grid load while also improves the navigation benefits. This study provides theoretical analysis with its practical applications in a real hyropower station as a case study for solving hydropower scheduling problems.

Unit Commitment Problem with Energy Storage Under Correlated Renewables Uncertainty

Unit Commitment Problem with Energy Storage Under Correlated Renewables Uncertainty” introduces a novel approach to address the challenges of renewable integration. The study acknowledges the growing variability and correlation in power availability due to renewable generation and proposes a day-ahead unit commitment (UC) problem formulation that incorporates energy storage and considers multistage correlated uncertainty. Using a variant of the stochastic dual dynamic programming (SDDP) method, which can handle temporal correlations effectively, the researchers solve the complex UC problem. Results obtained from the IEEE 118-bus system demonstrate the significant advantages of considering multistage uncertainty and correlations. Applying their approach to the Chilean power system, the researchers present superior UC solutions that adapt generation to changing uncertainty at a lower cost. Additionally, they propose a more efficient deterministic UC solution that outperforms current industry practices. These advancements promise to enhance the integration of renewable energy sources, enabling a more sustainable and environmentally friendly future.

Hybrid CHGSO Approach-Based EV Energy Regulation by Considering Incentive-Based Prosumer and Uncertainty

This paper proposes the hybrid approach for increasing the profit of prosumers and reducing the energy cost of the residents. The hybrid method is the combination of chaotic maps and the Henry gas solubility optimization algorithm (HGSO), hence it is called chaotic Henry gas solubility optimization (CHGSO). The proposed approach is the community-based microgrid that incorporates energy storage systems (ESS), electric vehicles (EVs) and photovoltaics (PV). Both ESS and EV are conserved as alternative sources of energy. The stored EV energy is used as a power source for homes and the grid by utilizing the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes. The power flow of EV/ESS is controlled depending on the rate of charging and discharging. Here, the battery degradation cost is considered. The proposed approach solves the optimization problems, like battery degradation of EV/ESS, regulation of charge/discharge and provides optimal economic benefits. The proposed approach increases the prosumer profit through encouraging EV energy transactions through incentive-based pricing. Finally, the CHGSO method is performed in MATLAB and is compared with existing methods. From the simulation analysis, it can be concluded that the cost of CHGSO method is less and the prosumer participation has increased.

Optimal deloading of PV power plants for frequency control: A techno-economic assessment

Due to the lack of inertial support from photovoltaic (PV) systems, large-scale PV integration into the existing grid poses a major threat to frequency stability. To address this, numerous techniques for incorporating energy storage systems have been proposed in the existing literature, but they incur significant operational costs. PV systems with deloading mechanisms have been researched as an alternative efficient strategy in many studies as a key frequency response support after a disturbance. However, deloaded PV systems incur a cost burden due to idle reserve capacity during regular operation. Consequently, an optimum deloading of PV systems is required to enhance the grid's frequency stability while minimizing its total operating cost. To that aim, this paper proposes a gradient descent-based optimization method to determine the optimum deloading of PV systems. The optimization considers both frequency response constraints (frequency nadir and rate of change of frequency – ROCOF) and operating cost constraints (generation, up-regulation, and value of lost load cost). The proposed technique is employed in a modified IEEE 39 bus New England System for varying levels of PV integration under two distinct cases. The optimization and relevant simulations are performed in Python and DIgSILENT PowerFactory platforms. In the case of a major generator outage with PV penetrations of 30%, 40%, and 50%, the optimal deloading percentages are 6.96%, 5.931%, and 4.968%, respectively, with no load shedding. Moreover, in the interconnection outage case, the optimal deloading percentages increase to 14.92%, 13.3%, and 12.42%, respectively, with load shedding. Nevertheless, in each case, deloaded PV systems consistently preserve frequency stability in the grid while incurring minimized operating costs. Furthermore, the performance and cost of the proposed solution are compared to two other primary frequency response support systems, viz. Battery Energy Storage System (BESS) and Synchronous Condenser (SC). The optimally deloaded PV system outperforms both these devices considering both technical and economic aspects.

Design and Energy Analysis of Photovoltaic-Battery Prototype Considering Different Voltage Levels

Photovoltaic self-consumption systems are effective at reducing energy consumption from fossil fuels and carbon emissions. Incorporating energy storage into these systems enables improved energy management and the optimization of their operation. However, to date, few studies have evaluated and compared the energy performance of PV systems with battery storage. In this context, with the current development of High Voltage batteries, research is needed on energy storage at different voltage levels incorporated into PV systems for self-consumption. In this way, the design and operation of an experimental prototype are described, consisting of two photovoltaic systems for self-consumption with energy storage using batteries operating at different voltages. One of them operates at low voltage (Low Voltage Installation, LVI) and the other at high voltage (High Voltage Installation, HVI). Through experimentation, it was demonstrated which installation is more efficient. During commissioning, the results showed the importance of considering the voltage level parameter in a renewable energy production system for self-consumption, since the energy yield of the HVI inverter-battery set was higher than in the case of the LVI on almost all of the days of the month studied. In addition, both systems showed a strong dependence on weather conditions, causing higher energy losses in their components during days with lower solar energy production. Therefore, the need for further analysis of energy efficiency to optimize the integration of these systems into the building sector was demonstrated.

Assessing flexibility options in electricity market clearing

This work presents a model to co-optimize the energy and reserves markets, taking into account the penetration and participation of various flexibility providers in both markets. In particular, a detailed unit commitment model has been developed based on mixed-integer programming techniques incorporating energy storage systems with both charging and discharging options, electric vehicles with both grid-to-vehicle and vehicle-to-grid modes, and demand response programs for cost-optimal energy and ancillary services scheduling. The balancing services considered include Frequency Containment Reserves (FCR), automatic Frequency Restoration Reserves (aFRR), and manual Frequency Restoration Reserves (mFRR), in both upward and downward directions. The impact of all these flexibility providers on operational and economic aspects has been assessed through an illustrative case study of a power system with high penetration of renewable energy sources, including thermal and hydroelectric power units. The results highlight the superiority of results when considering the participation of all flexibility providers, especially in the ancillary services market, in terms of economic competitiveness, renewable energy curtailment, associated CO2 emissions, and utilization of costly energy resources. The growing share of flexibility providers in both energy management and reserve provision mix highlights the importance of these sources for power mixes with low carbon content. The methodological framework developed can be employed by system operators, market participants, and policymakers to provide price signals and optimize their resources and portfolios.

Selection of energy storage technologies under neutrosophic decision environment

The variability of renewable energy can be addressed by incorporating energy storage technologies (ESTs). Multiple energy storage technologies are available for renewable energy with wide disparity in attributes. Reports regarding the attributes are conflicting and complex, which makes it a multi-attribute decision analysis problem. To address this issue, new decision support tools are needed to aid in selecting ESTs based on their technical, environmental, and economic characteristics. In this study, a neutrosophic data envelopment analysis (NDEA) technique is developed and applied to selection of ESTs considering neutrosophic uncertainty inherent in new technologies. The decision tool systematically evaluates EST options by considering the expert's degree of satisfaction for higher technical performance, degree of dissatisfaction for lower technical performance and degree of indeterminacy for attaining better technical performance. Two case studies adapted from literature are used to illustrate the decision support tool. The first case study, which is used to demonstrate the tool for EST selection, involves the selection of thermal energy storage options. The second case study involves eight ESTs with different mechanisms: supercapacitor, flywheel, compressed air energy storage, pumped hydro energy storage, Li-ion battery, NiCd battery, NaS battery, and Pb-acid battery. The results reveal certain ESTs that are preferred over wide range of expert's perception to uncertainty in terms of dissatisfaction and indeterminacy. For example, Li-ion battery is DEA efficient if the risk tolerance for indeterminacy is greater than 40% and Pb-acid battery is DEA efficient at falsity tolerance greater than 80% and indeterminacy tolerance of less than 5%. These alternatives can be regarded as robust choices. Insights gained from the application of the decision tool can be used for guiding investment decisions in planning of renewable energy systems with energy storage.

Renewable Energy Microgrids to Improve Electrification Rate in Democratic Republic of Congo: Case of Hydro, Municipal Waste and Solar

Worldwide, it is imperative for citizens to have access to electrici-ty. This applies to Congolese--rural and urban dwellers, and if possible, it should be guaranteed by government’s laws and poli-cies. However, the rural and urban areas of Democratic Republic of Congo (DRC) suffer majorly from lack of access to electricity. The major reasons are the high costs associated with connection to the national central grid and production insufficiency. There-fore, one feasible approach to electrify these areas is to use mi-crogrids. This technology is decent and viable option for energy revolution since it incorporates energy storage systems, distribut-ed generators, and localized loads. This paper has taken to im-plement this solution by firstly analysing some cities located at the borders of large rivers or watercourses (with known depth and width), such as the Congo River considered for hydrokinetic pow-er (HKP). However, where the Congo River does not pass through, the paper will consider largest rivers passing in the area. For the case of photovoltaic electricity production, large cities are considered those with good sunshine and large population who have purchasing power for the photovoltaic electricity. The waste to energy power plans will consider the top ten densely populated cities in DRC. The proposed microgrids will operate in isolation (islanded) mode. This paper proposed 44 projects to generate 795 690 kW total energy from the microgrids. These energies are divided as 661 000 kW from solar photovoltaic, 83 790 kW from waste to energy, and 50 900 kW from hydrokinetic generation. The urban share will be 94.9% and rural area share will be 5.1% of this generation. Further work needs to include biomass as a possible renewable energy to add in the mix.

Utilization of energy storage devices with optimal controller for multi-area hydro-hydro power system under deregulated environment

This paper presents an endeavor to demonstrate the design, implementation and analysis of an interconnected multi-area multi-unit hydro-hydro power system operating in deregulated environment incorporating energy storage (ES) devices. To efficiently tackle the non-minimum phase characteristic of hydro turbine, optimal controller (OC) is designed using full state feedback control strategy. The efficiency of OC is evaluated under all realistic transactions i.e., poolco, poolco-bilateral and poolco-bilateral with contract infraction, and then the study is extended by introducing the different energy storage devices in the system to compare OC performance in the presence of each energy storage device. Further to analyze the robustness of OC, the system’s dynamic performance is observed by including the nonlinearities of the system as well as throughamplevariation insystem parameters. Simulation results demonstrate that OC is able to meet automation generation control (AGC) requirement for all possible power contracts and thedynamic performance of the system with redox flow battery (RFB) is found superior to the rest of ES devices. Sensitivity analysis along with the performance of OC with system nonlinearities validate its robustness.

Resilient energy management incorporating energy storage system and network reconfiguration: A framework of cyber‐physical system

AbstractDue to increasing the intricacies of cyber‐physical systems (CPSs) and the severity of natural phenomena, upgrading network planning is vital to reduce the vulnerability of these systems. This study develops a novel preventive‐corrective resilient energy management strategy (PC‐REMS) for a CPS in two stages, exploiting the network reconfiguration (NR) and energy storage systems (ESSs) capacity. The first stage of the proposed PC‐REMS follows preventive actions based on contingency faults. In contrast, the second stage applies corrective measures for improving the CPS resilience to cope with natural physical disasters. Vulnerability assessment data is sent to the physical power system daily through the communication network. The first stage of preparing the CPS for predictable faults focuses on pre‐scheduled ESSs and preventive NR to minimise the expected energy curtailment cost. The second stage involves the network recovery in real‐time through corrective NR to minimise energy curtailment cost after the faults. Three resistance, recovery, and resilience indices are introduced for evaluating the effectiveness of the model. The proposed model is examined by performing multiple simulations on the 33 and 118‐bus radial test systems. The simulation results show the efficiency of the proposed PC‐REMS model in dealing with predictable disasters to improve the CPS resilience.

Multiobjective optimization of hybrid wind-photovoltaic plants with battery energy storage system: Current situation and possible regulatory changes

The challenges presented by increased electricity generation from intermittent renewable energy sources can be minimized by incorporating energy storage systems (ESS). Despite the benefits, this is still an emerging technology with limited use in Brazil. The aim of the present study is to use a multiobjective optimization process to support the planning of hybrid wind-photovoltaic projects with utility-scale Li-ion battery ESS. Levelised cost of energy (LCOE), diversified energy production density, and net present value are considered as the objectives. The multiobjective optimization is conducted in view of the possible impact of regulatory adjustments necessary for the integration of ESS in the Brazilian context. The optimization problem has been formulated using the mixture arrangement technique and the Normal Boundary Intersection approach is adopted for search in the design space. It is shown that in the current scenario, the possibility of integrating storage reaches only 17.7% of the project capacity. In addition, the investment cost in ESS impacts the viability of the project more than the payout for the service provided. Thus, regulatory adjustments must (i) allow the generation of multiple revenues and avoid double taxation; (ii) predict whether there will be a defined minimum payment for services rendered and/or whether there will be a subsidized credit line; and (iii) develop the production chain that involves storage or create tax incentives for importing equipment to reduce costs. Finally, it is important to highlight that the ESS imposes an increase in the load for the system as a whole and this characteristic needs to be considered for long-term planning.

A home energy management model considering energy storage and smart flexible appliances: A modified time-driven prospect theory approach

Smart home is a small but an important energy segment that has a significant potential to implement authentic energy policies, where human is a major decision-maker in the home energy management dilemma. Therefore, humans’ emotions and tendencies plays a vital role as the End-User's daily decisions. In this paper, we develop a behavioral home energy management model based on time-driven prospect theory incorporating energy storage devices, distributed energy resources, and smart flexible home appliances. The conventional prospect theory is then modified by considering the psychological time discount effect in the value function. The non-flexible load and photovoltaic generation uncertainties are demonstrated by a scenario-based uncertainty modeling approach. The proposed methodology is a Mixed Integer Linear Programming (MILP) model which is solved by the CPLEX solver in the GAMS software environment. The proposed methodology is verified by comparing the results from conventional and behavioral perspectives. In addition, the role of electrical energy storage and smart flexible home appliances are investigated clearly. The obtained results of the current study are compared with previous conventional home energy management studies to show the effectiveness of the proposed methodology. The results of the simulation studies show that the End-User's satisfaction in the proposed home energy management behavioral structure will be increased substantially compared with the conventional monetary home energy management models.

The development of a fuzzy tilt integral derivative controller based on the sailfish optimizer to solve load frequency control in a microgrid, incorporating energy storage systems

In recent years, the increasing use of distributed generation technologies in the form of MicroGrids (MGs) has raised the risk of frequency instability. Since, the total energy is derived from unreliable renewable energy resources (RESs). In such cases, offering an MG model with an efficient load frequency control (LFC) is critical for restoring the unstructured power system stability. This research looks into a LFC design for a multi area MGs with renewable energy resources. The suggested Fuzzy based Tilt-integral-derivative (FTID) controller parameters are optimized using the Sailfish Optimizer (SFO) to solve the load frequency control (LFC) problem. The optimum range fuzzy membership and Tilt-integral-derivative (TID) controller gains are determined by minimizing the fitness function, i.e integral time absolute error (ITAE). For an MMG complete system, a SFO-based FTID controller has been proposed. Load fluctuations, wind speed variations, and sun irradiation are all included in the scenarios, which are based on real-world data. Finally, using OPAL-RT, a real-time hardware-in-the-loop (HIL) simulation is performed to confirm the applicability of the proposed controller for the LFC problem in the MG from a systemic perspective.

Multi-Objective Generation Scheduling of Hydro-Thermal System Incorporating Energy Storage With Demand Side Management Considering Renewable Energy Uncertainties

Atmospheric pollutants mainly produced by thermal power plants compel to utilize green energy sources such as renewable energy sources and hydroelectric plants in a power system. But due to blinking behavior of sources of renewable energy and due to very high rate of outages, it has a detrimental consequence on overall grid. Demand side management (DSM) programs decrease cost and improve power system security. This study proposes non-dominated sorting genetic algorithm-II (NSGA-II) to solve multiobjective scheduling of generation for fixed head hydro-thermal system integrating pumped hydro energy storage and sources of renewable energy taking into consideration the outage and uncertainty in presence of DSM. Numerical results of the test system attained using the proposed technique were compared with strength pareto evolutionary algorithm 2 (SPEA 2).

A novel multi-objective stochastic risk co-optimization model of a zero-carbon multi-energy system (ZCMES) incorporating energy storage aging model and integrated demand response

To model a realistic and highly flexible zero-carbon multi-energy system (ZCMES), a novel modelling strategy for ZCMES incorporating energy storage aging influence and integrated demand response (IDR) is proposed. Firstly, an integrated clustering-scenario generation and reduction approach (IC-SGRA) is developed to quantify the datasets uncertainties while selecting a representative day for the model. Secondly, the model is formulated as a multi-objective optimization problem to evaluate the influence of decision-maker preference concerning investment cost and operation cost on the optimal planning, and then weighting sum method is adopted to solve the problem. Finally, a Markowitz portfolio risk theory approach is adopted to mitigate the risk associated with uncertainties during decision-making, then an illustrative case study is used to analyse the proposed model. The simulation results reveal that the energy storage is overdesigned when aging effects are not considered, and the proposed approach can reduce the investment cost and the operation cost by 10.86% and 80.66% respectively, while the overall expenditure is reduced by 23.09%. Moreover, it was observed that the optimal total economic cost is obtained when high preference is given to the operation expenditure by the decision-makers while an equal preference resulted in a 0.24% reduction in investment cost and a 0.49% increase in total expenditure. Furthermore, the effect of BES lifetime and IDR load factors are also examined on ZCMES optimal planning. This study concluded that IDR is a promising strategy to encourage adopting zero-carbon policies flexibly and economically while choosing BES with high lifetime and tolerable capacity loss contribute to optimal planning.

Probabilistic Modeling and Equilibrium Optimizer Solving for Energy Management of Renewable Micro-Grids Incorporating Storage Devices

Recently, micro-grids (MGs) have had a great impact on power system issues due to their clear environmental and economic advantages. This paper proposes an equilibrium optimizer (EO) technique for solving the energy management problem of MGs incorporating energy storage devices concerning the emissions from renewable energy sources (RES) of MGs. Because of the imprecision and uncertainties related to the RESs, market prices, and forecast load demand, the optimization problem is described in a probabilistic manner using a 2m + 1 point estimation approach. Then, the EO approach is utilized for solving the probabilistic energy management (EM) problem. The EM problem is described according to the market policy on the basis of minimizing the total operating cost and emission from RESs through optimal settings of the power generated from distributed generators (DGs) and grids connected under the condition of satisfying the operational constraints of the system. The proposed EO is evaluated based on a grid-connected MG that includes energy storage devices. Moreover, to prove the effectiveness of the EO, it is compared with other recently meta-heuristic techniques. The simulation results show acceptable robustness of the EO for solving the EM problem as compared to other techniques.

All-solid-state electrochromic Li-ion hybrid supercapacitors for intelligent and wide-temperature energy storage

Newly proposed electrochromic Li-ions hybrid supercapacitors (ELHSs), incorporating energy storage and electrochromic functions into one hybrid system, provide huge potential for next-generation portable and intelligent electron devices. The critical next step in the future implementation is to explore a high-rate and stable battery-type electrochromic positive electrode to match the capacitive electrochromic negative electrode even under harsher environments. Herein, spinel LiMn2O4 electrodes coating with LiNbO3 thin layer (namely LMO@LNO) possess interconnected microstructures and multipores are successfully fabricated and severed as high-performance battery-type electrochromic positive electrode for ELHSs. The resulting LMO@LNO electrode exhibits remarkable electrochromic and energy storage performances, including large optical modulation (∼42.1%), high specific capacity (127.6 mAh g−1) and robust long-term electrochemical stability (over 1000 cycles). The Li-ion migration kinetics and electrochromic mechanism of the positive electrode are further comprehensively analyzed through experimental characterizations and density functional theory (DFT) calculations. By pairing with a transparent WO3 electrochromic capacitor-type negative electrode, an all-solid-state ELHS with a maximum working voltage of 2.3 V is assembled, delivering an impressive energy/power density (106.1 Wh kg−1/574.7 W kg−1) and admirable capacity retention of 83.5% after 3000 cycles. Significantly, the as-obtained ELHS with excellent environmental compatibility and reversible electrochromic performance even operate at progressively varying temperatures (RT ∼ 60 °C). This work provides a new opportunity to design next-generation safe and intelligent hybrid energy storage system for consumer electronics.

A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs

The use of photovoltaic (PV) systems has drawn attention as a solution to reduce the dependence on fossil fuel for building energy needs. Moreover, incorporating energy storage systems (ESSs) in PV systems can optimise electric energy costs by increasing dependency on PV-generated energy during electric peak load times. However, current ESSs have limited capacities making it difficult to fully maximise PV-generated energy. We propose a novel integrated energy-efficient system for PV, ESS and electric heat pump (EHP) that maximises the usage of PV energy, optimises ESS usage and reduces EHP energy consumption costs. The components of the proposed integrated system are linked with a deep learning (DL)-based algorithm that forecasts PV energy generation and energy demand of the EHP. The proposed system schedules the charging/discharging time of ESSs depending on peak load times, the forecasted EHP electric demand, and PV-generated energy. The data used were collected for 10 months from a retail shop equipped with an EHP and ESS. We found that the developed DL-based forecasting models for PV and EHP are accurate and reliable (ie, R2 above 0.95). Also, the results show that the proposed integrated energy-efficient PV-ESS-EHP system saves 12% of the total annual electric costs, which corresponds to 1 285 291 Won. The proposed system ensures an efficient method to maximise PV-generated energy resulting in reduced dependency on fossil fuels for building energy needs.

Enhancing the fresh water produced from inclined cover stepped absorber solar still using wick and energy storage materials.

Renewable energy-based desalination (RED) technique is gaining more importance over the desalination techniques as it appeared to be a promising technique towards low-cost desalination for sustainable growth as the energy demand towards other developments is continuously increasing. This study aims to incorporate energy storage materials and wick materials in the inclined solar to improve the fresh water produced. In this work, the performance of inclined solar still using coconut coir disk and energy storage material is analyzed experimentally. Characterization results showed that the porosity, absorbency, capillary rise, and heat transfer coefficient of 1-year dried coconut coir disk were found as 73.25%, 2 s, 10 mm/h, and 37.21 W/m2K, respectively, which is higher as compared with that of other coconut coir disks. The experimental results on the performance showed that total distillate from novel inclined solar still with hybrid techniques (energy storage materials and wick materials) were recorded as 3645, 4080, 4570, and 4890 ml for 3 months, 6 months, 9 months, and 1 year, respectively. While the total distillate outputs of an inclined solar still with only wick materials were recorded as 2560, 2670, 2930, and 3390 ml for 3 months, 6 months, 9 months, and 1-year coconut coir disk wick materials. The novel hybrid techniques used in the inclined solar still with energy storage materials enhanced the yield from 82.25 to 144.5% than the conventional solar still, whereas the inclined solar still with only wick materials enhanced the yield from 28 to 69.5% than the conventional solar still.

Incorporating energy storage and demand response into intentional controlled islanding using time decomposition

Incorporating energy storage and demand response into intentional controlled islanding using time decomposition