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—To copy with the peak valley difference and peak load problem of power distribution network (PDN), this paper proposes a distributionally robust planning model for PDN considering the flexible management via the multi-energy station (MES) based integrated demand response (IDR). The planning covers reinforcement of the existing substation and feeders as well as the investment of MES and IDR. The IDR involves the incentive interruptible load response (ILR), transferable load response (TLR) and price-based power-gas substitution (PGS) which is implemented on MES. The MES integrates multiple energy storages and energy coupling devices, which is served as a platform for PDN operator to centrally track the final demand and improve the response potential of IDR. The proposed planning method aims at minimizing the present value of PDN investment cost and operation cost during the planning period. To handle the uncertainty of PDN end-use loads, a two-stage Kullback–Leibler divergence (KLD)-based distributionally robust optimization (DRO) planning model is formulated. Finally, the columns and constraints generation algorithm is utilized to solve the KLD-DRO planning model. Case studies are carried out on a practical 152-bus urban distribution system to validate the effectiveness of the proposed methodology and quantitatively analyze the key influence factors of PDN investment. • The multi-energy station based integrated demand response is carried out. • The incentive interruptible load, transferable load and price-based power-gas substitution are all considered. • A KL divergence-based distributionally robust planning model is formulated. [ABSTRACT FROM AUTHOR]

Liquid hydrogen (LH 2) can serve as a carrier for hydrogen and renewable energy by recovering the cold energy during LH 2 regasification to generate electricity. However, the fluctuating nature of power demand throughout the day often does not align with hydrogen demand. To address this challenge, this study focuses on integrating liquid air energy storage with a power generation system based on LH 2 regasification. Three configurations are proposed and compared: no-storage, partial-storage, and full-storage. In the no-storage system, power is generated using recuperated Brayton cycle and two organic Rankine cycles without energy storage. In contrast, the partial-storage system offers flexible operational modes. During peak times, cold energy is utilized for power generation, while it is diverted to store liquid air during off-peak times. In the full-storage system, produced energy is partially reserved throughout the day. Thus, compared to the partial-storage system, the full-storage system boasts a simplified configuration, eliminating the need for operational mode transitions. Furthermore, its larger liquid air storage capacity maximizes the difference in power production up to 4.53 MW between on- and off-peak times. Consequently, despite its higher capital costs, the full-storage system demonstrates greater economic viability, especially when considering government subsidies. [Display omitted] • Waste cold energy from LH 2 is recovered and utilized for power generation. • Liquid air energy storage is integrated into power generation for peak load shaving. • Operating conditions are optimized using the Bayesian optimization algorithm. • Economic feasibility of proposed systems is evaluated under various scenarios. • The full-storage system is most effective and economical for peak load shaving. [ABSTRACT FROM AUTHOR]

A reasonable regional integrated energy system (RIES) structure is the key to achieving its economic and efficient operation. However, in actual engineering construction, only the maximum load is considered as a prerequisite for energy design, which leads to waste of energy allocation. In view of this, this article proposes an electricity-thermal-cooling RIES energy stations and networks planning model that coordinates the operation of multiple-user loads, energy networks, and energy stations based on the idea of load clustering. Firstly, an average peak valley difference rate covering electricity, thermal, and cooling loads was proposed to evaluate the load characteristics of the RIES. Secondly, based on the load cluster characteristics and network costs of RIES, a RIES planning optimization framework is proposed, which includes load cluster division, RIES site selection and network planning, and RIES capacity planning. Thirdly, based on the P-median model and load cluster characteristics, establish an RIES location and network layout model. Fourthly, establish an RIES capacity planning model with the goal of annual total cost and carbon emissions. Finally, Dijkstra, improved particle swarm optimization, and VIKOR were used to solve the model. The results showed that the proposed model reduced the annual total cost by 7.13 % and the annual carbon emissions by 3.72 %. • Establish an average load peak valley difference index. • Establish a RIES planning framework includes network layout and capacity planning. • Establish an IES planning model with the goal of cost and carbon emissions. • Total annual cost decreased by 7.13 % and CO 2 emissions decreased by 2.77 %. [ABSTRACT FROM AUTHOR]

Increasing flexibility of energy demand in a district heating network (DHN) results in both more possibility of interactions between the heating network and renewable resources and managing peak loads by shaving and shifting. This paper summarizes a series of tests performed in a large building complex that may drive future operational strategies for DHN. These novel strategies aim to impact selected customers with a specific range of heat consumption intensity to assess achievable flexibilities in heating demand without additional cost. The results show that a reduction in DHN supplied temperature by 6 °C does not significantly influence the heat consumption of customers equipped with radiators and thermostatic valves. However, its reduction by 10 °C, and 20 °C can impact users with higher consumed heat rates of approximately 0.2 and 0.1 [Unit/10min], respectively. Moreover, prolonging test periods until midday, when there is the highest probable outdoor temperature and solar irradiation may avoid sharp energy variations after tests. • Some trials were conducted in residential areas to find DHN flexibility options. • Some trials were implemented to impact just customers with high energy demands. • The perspective of energy management in next generation of DHNs is carried out. • Some scenarios evaluated to peak shaving and load shifting of DHN in residential area. [ABSTRACT FROM AUTHOR]

Dynamic performance prediction of the PCM-based double-pipe latent heat thermal energy storage (LHTES) system plays an important role for building peak load shifting. This study presents a stacked ensemble learning framework for the phase change performance prediction of LHTES systems. The proposed model has a two-layer structure involving base and meta models: Regression Tree, Support Vector Regression and Linear Regression. Sensitivity analysis has been utilized for the feature extraction considering possible geometrical and physical parameters of LHTES systems with a fixed PCM volume that can store 0.94 kWh of energy within 255 min. Case studies show that the proposed model outperforms the base models in terms of both prediction accuracy and robustness. Compared with base models, the proposed model has a maximum 7.82% (charging) enhancement in Mean Absolute Percentage Error (MAPE) over all-range data sets and a maximum 16.43% enhancement in MAPE for the initial discharging stage, and has more stable predictive results within the high accuracy zone (accuracy > 95%) with a maximum 7.3% (charging) increase in the distribution density over all-range data sets and a maximum interquartile range reduction of 25.6% (middle discharging stage) in Mean Absolute Error. Application of the proposed model achieved more building peak load reduction. • Stacked ensemble learning-based framework for phase change prediction. • Sensitivity analysis is introduced for key feature selection. • Prediction accuracy is enhanced with a minimum 3.06% of MAE for charging process. • It is more robust and 90.7% of results have an accuracy higher than 95%. • Having great potentials for applications towards flexible building energy. [ABSTRACT FROM AUTHOR]

Flexibility can support the energy transition and improve the efficiency of heat supply to buildings. This study tests price models for the inclusion of heat pumps (HPs) in district heating network (DHN). Various price models were tested in two cases in Sweden. In Sweden, DH companies are increasingly confronted with instances where building owners invest in a HP and only use DH when it is too cold for the HP to function efficiently. This poses challenges for DH companies due to the high costs associated with peak load production. Potential gains in HP-DHN flexibility and the investment and operational risks of HP-DHN integration are therefore important topics to understand and investigate. Our results show that HPs can increase the flexibility of DHNs, reduce costs and environmental impact. We also identify that the lowest risk exposure to DH companies, in HP-DHN arrangements, is when the building owner invests in the HP and the DH company operates it. • At the residential building level, the integration of HPs into the heat supply from DH companies contributes to enhanced flexibility which reduces costs and emissions. • Using price models that reflect real-time costs of producing heat, allows load shifting between a primary (DHN) and secondary heating system (onsite HP system). • The risk exposure is linked to the interplay between HP investment and HP operation [ABSTRACT FROM AUTHOR]

In recent decades, the ever-increasing demand for electricity in large cities is leading the electricity distribution network (EDN) to an operational state of fatigue, especially at peak load times. In this context, load congestion, technical losses, and voltage deviations across the EDN can contribute to the high waste of electricity as well as to the worsening of the supply service. To face this problem, this paper proposes a mixed integer linear programming (MILP) model that aims to reshape the demand profile (i.e., load reduction in peak periods without load rebound in the remaining periods of the day) of total consumers while the voltage deviations and technical losses are minimized. In the proposed model, the demand profile reshaping is carried out through direct (DC) and indirect (IC) bulk control of residential, commercial, and industrial loads, while the load factor (LF), i.e., indicator related to the efficient use of electricity, is improved. Due to the scheduling of these loads at certain times of the day, the occurrence of voltage deviations and technical losses can be mitigated by the efficient control of capacitor banks (CBs). To corroborate the applicability of the proposed MILP model, the 33-node IEEE test system was used. The results show the technical and economic gains for the distribution company (DISCO) and the total number of consumers. • A MILP model is proposed to reduce electricity waste in EDN. • DC and IC of bulk loads to reduce peak demand without occurrence of load rebound. • Control of CBs to mitigate EDN under and over voltages due to load variation. • Improvement of LF related to the EDN demand profile to alleviate operational stress. [ABSTRACT FROM AUTHOR]

Fossil fuel power plants continue to contribute significantly to carbon emissions, necessitating a transition towards cleaner energy sources. Despite the growing presence of renewables within the power systems, the incorporation of carbon capture technologies into the traditional thermal power plants holds great potential in emissions reduction. In this paper, the integration of renewable energy sources (RES) and coal-fired power generation units outfitted with carbon capture schemes is addressed. Multiple demand response (DR) programs and hydropower plants are strategically utilized to increase the power system flexibility. To effectively plan the day-ahead (DA) operation of the power system, a presumed market-clearing framework is adopted and modelled as a risk-constrained two-objective stochastic mixed-integer linear programming problem. The proposed framework helps to tackle the uncertainties related to RES and demand variations by employing a hidden Markovian process (HMP) technique. To simultaneously minimize the system's operational costs and CO 2 emissions, an enhanced version of the augmented ɛ -constraint method is employed. To prove its value, the proposed framework is devoted to the 24-bus IEEE reliability test system (IEEE-RTS). The system features substantial penetration of RES (exceeding 87% of peak load) and standard DR options capacities (less than 25% of peak load). The results show a 24% reduction in load peaks, an over 63% decrease in emissions, and a 17% reduction in the overall operation costs. • Proposing a comprehensive linearized mathematical framework accelerating the energy transition. • Coordinating the responsive power systems with carbon capture and hybrid ESS (BESS, PHS, HES). • Enhancing an effective augmented ɛ -constraint technique to simultaneously optimize cost and CO 2. • Presenting robust decision-making with two-stage SP, hidden Markovian process, and CVaR strategy. • Integrated approach: 87% of RES, cost & CO 2 reductions, fewer load peaks, enhanced flexibility. [ABSTRACT FROM AUTHOR]

Flexibility has increasingly gained attention within the field of electrification and energy transition where a common objective is to reduce the electricity consumption peaks. However, flexibility can increase the risk of grid congestion depending on where and when and it is used, thus an overall system perspective needs to be considered to ensure an effective energy transition. This paper presents a framework to assess electricity users' contributions to grid load peaks by splitting electricity consumption data into subsets based on time and temperature. The data in each subset is separately correlated with the grid load using three correlation measures to assess how the user's consumption changes at the same time as typical grid peak loads occur. The framework is implemented on four different types of business activities at Uppsala municipality in Sweden, which is a large public entity, to explore their behaviors and assess their grid peak load contributions. The results of this study conclude that all four activities generally contribute to the grid peak loads, but that differences exist. These differences are not visible without splitting the data, and not doing so can lead to unrepresentative conclusions. The presented framework can identify activities that contribute the most to unfavorable grid peaks, providing a tool for decision-makers to enable an accelerated energy transition. • Assess electricity consumer's contributions to grid peak loads using correlation. • Data is split to reveal different and opposite behaviors that otherwise are not visible. • The suggested framework includes important features without complex modeling. • Four different types of consumers are tested, showing different results. • Results can be used to decision-making about smart grid services, e.g. user flexibility. [ABSTRACT FROM AUTHOR]

This study uses a cryogenic distillation method air separation unit (ASU) coupled with liquid air energy storage (LAES) to improve the round-trip efficiency and reduce the initial investment of LAES. We propose an integrated system (ASU–LAES–DER) with different ways of energy release. In the novel system, the liquid air can be reheated and expanded to produce additional power, or the liquid air energy stream and part of the material stream can be used for ASU distillation to produce power saving effects. This novel integration allows the ASU to have load regulation capability and facilitates the implementation of demand-side power management. Furthermore, the flexible and efficient energy release methods improve the energy efficiency and economic value of LAES. The comprehensive round-trip efficiency of the system can reach 52.1%–69.2 % with a payback period of 3.25–6.72 years. The peaking index is used to analyze the peak-to-valley load transfer capability of the novel system. Results show that the peaking index ranges from 25.5 MW to 50.1 MW at different scales, indicating that the system can be used as an important grid peaking unit when applied on a large scale. • A novel system with flexible utilization of liquid air cold energy was proposed. • Cold balance during integration was achieved by storing ASU's by-products. • The comprehensive round-trip efficiency improved by 3.0%–20.1 %. • The payback period is 3.25–6.72 years. • The novel system with peak load shifting enables large-scale energy storage. [ABSTRACT FROM AUTHOR]

Coal-fired power plants are forced to participate in deep peak shaving of power grid. As load of peak shaving unit further decreases to 30 % or less, operation economy of coal-fired unit at ultra low load has become one of the main focuses of concern after unit flexibility. Excessive air coefficient, as an important technical parameter affecting economic operation of unit, can be enhanced to maintain combustion stability at low loads under stationary fuel boundary condition (SFBC) and stationary steam parameters boundary condition (SSBC). To obtain energy consumption characteristics of 660 MW supercritical unit during steady-state processes of SFBC and SSBC, the model of influence of excess air coefficient on overall performance and main parameters of unit is further developed. Subsequently, quantitative evaluation and analysis of unit performance on variable excess air coefficient is executed under modes of SFBC and SSBC. Influence mechanism of excess air coefficient on operation parameters and energy consumption characteristics of unit is revealed and clarified. Pre-eminently, evolution mechanism for heat rate and relative internal efficiency of steam turbine, steam temperature and coal consumption rate is given quantitatively and firstly. Results demonstrates that, SSBC mode is more economical for unit running with excess air coefficient lower than 1.3. With excess air coefficient higher than 1.4, economic difference between two operation modes is small. However, load as low as 30 % boiler maximum continuous rating (BMCR), SFBC mode shows its advantages, which is more suitable for peak shaving of ultra low load. Maximum difference in coal consumption under two operation modes is up to 0.5 g/kWh corresponding optimal operating range of excess air coefficient at 30 % BMCR. This study is expected to provide a detailed reference for upgrading operation economy of coal-fired power plants during low load peak shaving. • Modeling of impact of excess air coefficient on entire unit performance is enforced. • Economic operation mode of unit is determined with variation of excess air coefficient. • Operating performance of unit under two boundary conditions of boiler is compared. • Coal saving amount of different running mode for unit is given under ultra-low load. [ABSTRACT FROM AUTHOR]

Accurate forecasting of peak electricity load has long been an active area of research in electricity markets, and power systems planning and operation. Unanticipated climate-induced surges in peak load can lead to supply shortages causing frequent brownouts and blackouts, and large-scale socioeconomic impacts. In this paper, the climate sensitivity of daily peak load is characterized by leveraging advanced statistical machine learning algorithms. More specifically, a rigorously tested and validated predictive model based on the Bayesian additive regression trees algorithm is proposed. Results from this study revealed that maximum daily temperature followed by mean dew point temperature are the most important predictors of the climate-sensitive portion of daily peak load. Among the non-climatic predictors, electricity price was found to have a strong positive association with the daily peak load. Economic growth was observed to have an inverse association with the daily peak load. While the proposed framework is established for the state of Texas, one of the most energy-intensive states with geographic and demographic susceptibility to climatic change, the methodology can be extended to other states/regions. The model can also be used to make short-term predictions of the climate-sensitive portion of daily peak load. • Maximum temperature is the most important predictor of climate-sensitive peak load. • Dew point temperature significantly influences peak load both during summer and winter. • Maximum sustained wind speed lowers daily peak load. • Electricity price has a strong positive association with peak load. • Economic growth of the state is inversely associated with peak load. [ABSTRACT FROM AUTHOR]

Fifth-generation district heating and cooling (5GDHC) is a promising solution to decarbonise future thermal energy systems. This paper investigates the potential of 5GDHC integrated with borehole thermal energy storage (BTES) in unlocking the synergies between heating and cooling via a bidirectional thermal network. A model for system design and operation simulation of 5GDHC is developed. In a case study, the proposed 5GDHC supplied by BTES facilitates the long-term balancing of heating and cooling demands despite the poor simultaneity between them. This enables 5GDHC to achieve overall advantages over modern heat pump driven systems, including lower levelized cost of energy, less greenhouse gas emission, higher exergy efficiency, and lower electricity peak load. One possible barrier of adopting 5GDHC is the high upfront cost, which mostly attributes to borehole drilling and booster heat pump installation. Demand scenarios with varying heating and cooling demand structures are analysed and show that the ratio of cooling to heating substantially affects the economic performance of 5GDHC coupled with BTES. When cooling to heating ratio is within the optimal range of 0.4–1.0, 5GDHC reaches lower levelized cost compared with modern heat pump driven systems and present-day gas-fired heating systems. • Evaluation of fifth generation district heating and cooling (5GDHC). • 5GDHC is coupled with seasonal borehole thermal energy storage (BTES). • BTES promotes synergies between heating and cooling demands by storing waste heat. • 5GDHC with BTES saves energy and cost compared with state-of-the-art thermal systems. • The cooling-to-heating ratio is correlated with the economic performance of 5GDHC. [ABSTRACT FROM AUTHOR]

Selecting an appropriate confidence level for chillers models could lead to better prediction of the peak electricity load, to more efficiently participate in demand response programs, and to manage the operation and performance of chiller plants. Therefore, this study aims to predict the power consumption of a chiller plant in a high-rise commercial building based on Quantile Random Forest (QRF). This study proposed a simplified data-driven approach to compute the lower and upper bounds of the prediction intervals (PIs) of power consumption for a chiller plant, consisting of four chillers. An evaluation of the proposed data-driven approach using measured datasets and computed PIs metrics demonstrates that the PI coverage probability for the samples in test datasets for all chiller models with the confidence level of 80% and 90%, are above 80% and 90%, respectively. Also, the results show that by increasing the confidence level from 80% to 90%, the upper and lower bounds of the demand charge differ from the actual value by a factor of 3.3 and 1.7 times greater, respectively. This approach has implications for developing sequences of operations for chiller plants in building automation systems for evaluating past performance and for achieving future optimization of these plants. • Developed a novel uncertainty-based chiller data-driven model method • Established the model independent of any building related assumptions • Demonstrated the reliability and accuracy of the model • Suggested real-world applications in building automation systems [ABSTRACT FROM AUTHOR]

With the rapid development of demand-side management, battery energy storage is considered to be an important way to promote the flexibility of the user-side system. In this paper, a Stackelberg game (SG) based robust optimization for user-side energy storage configuration and basic electricity price decisions is proposed. Firstly, this paper put forward a two-stage energy management framework considering the interactive relationship between the supplier-side system and the user-side system. Secondly, based on the two-part electricity price mechanism, a bi-level optimal sizing of user-side energy storage is established in which robust dispatching is considered to deal with the uncertainty of renewable energy. Thus, a three-layer optimization model of "pricing on the power supply side–basic scenario configuration on the user side–worst-case scenario scheduling on the user side" is formulated. Through relaxing the state variables of energy storage in the configuration and scheduling models and combining Karush-Kuhn-Tucher conditions, the user-side model is transformed into a single-layer problem. A distributed algorithm based on the method of bisection is used to solve the two-stage SG problem. The simulation results demonstrate the basic electricity price and energy storage configuration suggestions and prove the superiority of the proposed method. [Display omitted] • A Stackelberg game-based three-layer optimization method is established for power pricing. • Demand declaration in the worst scenario is considered in the user-side energy storage configuration. • A bisection-based distributed algorithm and binary variable relaxation method are applied. • The proposed model improves the supplier's economy and reduces the user's peak load. [ABSTRACT FROM AUTHOR]

Achieving sustainable development goals requires off-grid electrification of remote areas using sustainable energy systems (SES). However, capacity planning for SES is challenging due to the long-term need to meet fluctuating demand and the intermittent and unpredictable nature of renewable energy (RE) sources. The best system size for resilience and reliable load servicing of a 100% hybrid renewable energy system has been investigated in this study. The ideal system configuration for a hybrid solar PV, wind, and hydro energy system has been achieved by applying the multi objective genetic algorithm (MOGA) optimization technique to assess optimal size of the renewable energy system. The PV/Wind/Hydro system has the lowest NPC and COE with the best target capabilities among all the communities examined, provided the system lasts 25 years, according to the NPC study. The most ideal configuration (PV 88 kW, wind 18 kW, Hydro1 215 kW, and Hydro2 197 kW) investigated for deployment of hybrid power system at the specified site has NPC, total system cost, and energy cost of $163,238 and $517,141, respectively, and $0.35/kWh. Finally, the implementation of this innovative proposal demonstrated that reliable load serving is possible with 100% renewable energy without the use of an energy storage device. • Optimization of a 100% hybrid renewable energy system without an energy storage system. • Hydropower is used in two systems, for the base load and peak load. • Three objective functions are considered: COE, LPSP, and RE. • Peak load serving: The system restricts the electricity purchased from the grid. [ABSTRACT FROM AUTHOR]

Improving denitrification efficiency of supercritical unit for peak load regulation by changing feed water temperature has become a focus of thermal power engineering under wide load. However, the effect mechanism of feed water temperature on the performance of supercritical unit is indistinct absolutely at the intermediate point temperature (IPT)-setting mode, which obstructs the performance optimization and the excavation of energy saving of supercritical unit with a change of feed water temperature aims at improving denitrification efficiency of supercritical unit. For the purpose, a closed cycle theory in which thermal power unit operating parameters interact and form a closed influence mechanism was defined and proposed, with such a core ideology, the performance analysis and assessment models under IPT-setting mode is constructed on the differential theory, energy balance and thermodynamics method, and the accuracy of established model is verified. These developed models are applied to assess the operational performance and to identify energy saving potential of supercritical unit for peak load regulation. Subsequently, a typical 660 MW ultra-supercritical coal fired power plant is selected as a reference case. Not only a quantitative analysis of effect of increasing feed water temperature adding HP heaters on supercritical unit performance is performed, but the effect of reducing feed water temperature stopping HP heaters on supercritical unit performance is implemented as well at the IPT-setting mode. Finally, the influence mechanism of feed water temperature on boiler thermal efficiency, steam turbine thermal consumption rate, standard coal consumption rate, power generation, denitrification efficiency, NOx production, steam temperature and other operating parameters of supercritical unit is absolutely revealed on IPT-setting mode. The investigation results show that the sensitivity of feed water temperature to supercritical unit performance varies greatly in different range of feed water temperature, especially when the feed water temperature is heated to the near the pseudo critical temperature, a dramatic change would take place in operation parameters of supercritical unit. Such a viewpoint is presented rigorously that increasing feed water temperature does not always achieve energy saving for supercritical units, also contrary to the existing viewpoint. • Closed cyclic influence mechanism among multiple parametersofsupercritical unitis presented. •Quantitative analysismethods running at the IPT-setting modeare built. •Model considers emission performance and economic performance. •Influence mechanismof feed water temperature on different running parametersis revealed. •Operating conditionsofstopping of HP heatersand adding of HP heatersareexecuted. [ABSTRACT FROM AUTHOR]

A modified Allam cycle (Allam-Z cycle) with a simpler system was proposed and investigated using NG (natural gas)/O 2 combustion products mixing with the circulation CO 2 as the working medium for power generation with high efficiency, zero CO 2 emission and peak load shifting. The modifications are that all the working media are pumped to high pressure by pumps instead of compressors, the cold energy of both liquid oxygen and LNG is used for degrading the cooling water for CO 2 liquefaction and a set of regenerative heat exchangers are arranged for turbine exhaust heat recovery. The influences of turbine parameters on the performances of the cycle were investigated. The comparison was performed under the conditions of condensation temperature of 30 °C, turbine inlet pressure of 30 MPa, inlet temperature of either 700 °C or 900 °C and the turbine outlet pressures of Allam-Z cycle and Allam cycle are 7.21 MPa and 4 MPa respectively. The results show that the output power efficiency and the equivalent net efficiency of the Allam-Z cycle with full CO 2 capture are 43.64% and 40.83% respectively or 50.87% and 48.05% respectively, which are 2.15% or 2.96% higher than those of the Allam cycle under the same condition. • A modified Allam cycle was studied with combustion product and CO 2 as working medium. • Cycle system is simplified with higher turbine back pressure to eliminate compressor. • The cycle integrates power generation with CO 2 capture and energy shifting. • Equivalent net efficiency is adopted considering 1/4 ASU consumed off-peak power. • Equivalent net efficiency is 48.05% with turbine inlet parameters of 30 MPa/900 °C. [ABSTRACT FROM AUTHOR]

Abstract Industrial customers of electric energy can obtain the energy from different sources such as electricity market, reciprocal agreements, small-scale turbines, batteries, wind generation, solar panel generation and the demand response program (DeRP) participants. Due to volatilities in electricity market, the overall price of energy provision for industrial consumers cannot be determined. In this work, a new optimization approach is developed for modeling the uncertainty of pool yield rate. Also, a new operation method of risk-averse proposed and contrasted with risk-neutral method is suggested. In this process, DeRP is utilized in order to decrease the overall price of electric energy for industrial customers. To show the efficiency of proposed method, fare comparison is presented between the deterministic approach and proposed optimization model with and without considering the DeRP. Obtained results show that, implementing the DeRP, the energy provision costs are reduced using risk-neutral and risk-averse approaches, respectively. Highlights • The cost function for power purchase is suggested in this work. • A new optimization approach is developed for modeling the uncertainty. • A new operation method of risk-averse. [ABSTRACT FROM AUTHOR]

Abstract Despite various challenges and problems, electric vehicle (EV) technologies have been under real attention to be employed in different fields like power systems. Charge/discharge power of such vehicles can help power network to solve some of common problems available in this field like peak time problems. Also, penetration of these vehicles can help operators to enhance environmental performance of power systems. This paper purposed to determined advantages of EVs in power systems and environmental performance. In this paper, a bi-objective optimization model has been proposed for economic operation and environmental performance of intelligent parking lot (IPL) containing EVs under employment of time-of-use (TOU) rates of demand response program (DRP). To solve such a problem, ε-constraint and fuzzy decision making methods are utilized and results representing efficiency and effectiveness of employed techniques are presented for comparison. Studied sample model in this paper is composed of IPL connected to upstream net, renewable and non-renewable resources and hydrogen storage system. An MIP model has been used to model mentioned bi-objective problem and mentioned model is simulated under GAMS. Obtained results from simulations revealed that due to positive implementation DRP, total emission and operation cost of IPL have been reduced up to 3.99% and 1.83%, respectively. This means that both economic and environmental objectives are satisfied. Highlights • Optimal economic-emission operation of intelligent parking lot (IPL) is obtained. • Hydrogen storage system is used for more flexibility of energy management. • ε-constraint algorithm is used to solve bi-objective problem. • Fuzzy decision making method is used to determine trade-off solutions. • DRP is used to enhance economic and environmental operation of IPL. [ABSTRACT FROM AUTHOR]

Abstract In recent decades, importance of green source has been increased rapidly because of these resources reduce environmental pollution and have lower operating costs. Moreover, it is possible to utilize a multi-purpose energy center scheme to provide thermal and electrical energy by the large-scale users. In this paper, we proposed a new multi-objective model based on cost-emission, operation cost of large-scale user and efficiency of electrical storage system (ESS) and thermal storage system (TSS) by considering the peak load management. This problem is solved by compromise method based on fuzzy decision making model and new Pareto filter. The main model is explained as mixed-integer linear programing and is solved by using GAMS software and CPLEX solving method. Finally, the performance of the recommended method for solving the main model is extensively explained on the two case study and compared to validate the performance of main model. Highlights • Modeling of efficiency, cost and gas release as multi-objective problem. • Application of new fuzzy decision making method. • Consideration of peak load management to decrease the cost and gas release. [ABSTRACT FROM AUTHOR]

At present, the integrated energy system (IES) is designed and built separately according to the peak load of respective regions, without considering the combination of multi-regional energy, which will lead to low energy efficiency of equipment and decline of economic benefits. In this paper, considering heating networks, a multi-region IES collaborative planning model is proposed. For the ring-shaped heating network between the various regional CCHP systems, a heating network model is established considering the node flow balance, thermal energy-flow constraint and thermal loss balance constraint. Combining the energy balance constraint of the CCHP system and the heating network model, a mixed integer linear programming (MILP) model of the capacity of the multi-region CCHP system for the collaborative optimization is established. Finally, a comprehensive area in China Xiongan New Area is taken as an example for simulation analysis. In view of the four types of users, including business area, office area, residential area, industrial area, the two scenarios are deeply compared and analyzed. The saving rate of scenario 2 compared with scenario 1 is about 8.95%. The multi-region collaborative planning and optimize operation in this paper can greatly improve gas turbine utilization, reduce gas boiler configuration capacity, minimize thermal energy transmission loss, and significantly reduce operating costs. • Proposed a multi-region IES collaborative planning model. • Established the ring-shaped heating network model. • Established a MILP model of the capacity of the multi-region CCHP system. [ABSTRACT FROM AUTHOR]

To phase out fossil fuels, energy systems must shift to renewable electricity as the main source of primary energy. In this paper, we analyze how electrification can support the integration of fluctuating renewables, like wind and PV, and mitigate the need for storage and thermal backup plants. Using a cost-minimizing model for system planning, we find substantial benefits of electricity demand in heating, transport, and industry adapting to supply. In Germany, flexible demand halves the residual peak load and the residual demand and reduces excess generation by 80%. Flexible operation of electrolyzers has the most significant impact accounting for 42% of the reduction in residual peak load and 59% in residual demand. District heating networks and BEVs also provide substantial flexibility, while the contribution of space and process heating is negligible. The results are robust to restrictions on the expansion of the transmission grid. • We analyze the impact electrification has on the integration of wind and solar. • Flexible electrification halves the residual peak load and the residual demand. • Flexible operation of electrolyzers reduces residual demand by 30% already. [ABSTRACT FROM AUTHOR]

Accurate seasonal peak load (SPL) prediction of underground gas storage (UGS) is of great significance for enterprises to formulate scheduling plans. In this work, a novel two-stage model is proposed to predict it. First, improved complete ensemble empirical mode decomposition (ICEEMDAN) is used to decompose the seasonal peak load into several intrinsic mode function. Second, the highest frequency components are smoothed by using Gaussian smoothing (GS), and second highest frequency component is decomposed into several components by applying wavelet transform (WT). Third, long short-term memory (LSTM) optimized by improved sparrow search algorithm (ISSA) is utilized to predict them, and the prediction results of these components are recombined to obtain the final prediction results. Finally, the historical data of Wen 23 in the Zhongyuan gas storage group is taken as a case, and three key issues are discussed. The results: (1) Utilizing ICEEMDAN, GS, WT, and ISSA can effectively improve the prediction performance of the LSTM; (2) The prediction performance of one-step is better than that of multi-step; (3) The comprehensive error and stability evaluation index is reasonable. The following conclusion is reached: the established prediction model can be used as a reference for the development of the UGS scheduling platform. • An innovative two-stage model is developed to forecast the SPL of UGS. • The proposed model is compared concerning prediction accuracy and stability. • Comparison of the developed model and four original component models is given. • The different step predictions and rationality of error indicators are discussed. [ABSTRACT FROM AUTHOR]

Modelling wind profile is crucial to the adequacy assessment of wind-integrated generation system. This paper characterizes the wind profile as a wind vector that consists of wind speed and direction. A wind vector simulation model is proposed to produce long-term wind vector samples, whilst maintain the probability distribution of actual wind vector as well as its gusty characteristics. Such model is incorporated into sequential simulation process of wind-integrated generation system, while the wake effect is considered with Jensen model. Then, adequacy assessment procedure considering wake effect is developed. Collected wind vector data from four distinctive sites in North Dakota in US are used to verify the proposed model. The wind-integrated IEEE Reliability Test System (IEEE-RTS) is used to demonstrate the application of the proposed model and the procedure to adequacy assessment. The impacts of wake effect, peak load, and wind turbine type on system adequacy are investigated in detail. [ABSTRACT FROM AUTHOR]

As Plug-in Hybrid Electric Vehicles are environment friendly, it is envisioned as an alternative solution for the existing transportation system. However, simultaneous charging of electric vehicles may stimulate increase in peak load, causing voltage deviation and power loss issues. Different from other approaches, this paper presents an alternative way to tackle these issues. A novel two area distribution system, each following a different mobility pattern is presented. This model considers various sites namely optimal, midst, and unfit site for locating the charging stations in both the areas simultaneously. Moreover, the scheduling of the plug-in Hybrid Electric Vehicle is addressed using a meta-heuristic solving tool as it involves simultaneous scheduling of vehicles in both areas. To illustrate the cost reduction obtained with this model, the performance is evaluated in a standard test system namely IEEE 69-bus radial distribution system. The test results support the efficiency of the proposed two area framework and the new solving tool. The economic and the stability aspect of the system are addressed under various scenarios and several important findings are revealed. [ABSTRACT FROM AUTHOR]

This study focused on the effects of variable renewable electricity (VRE) on full load hours and energy efficiency of fossil-fired power generation in the European Union from 1990-2014. Member states were aggregated into three groups based on the level of VRE penetration. Average full load hours are found to be decreasing since 2006 for all groups. The decrease is most in the group with the highest VRE penetration level with a 53% decline from 2005 to 2014 (while VRE penetration increased from 8% to 25%). For VRE-medium the decrease was 34% from 2007 to 2014 (while VRE increased from 3% to 13%) and for VRE-low 32% (with 1% to 5% VRE penetration increase). Both the financial crisis and the share of VRE show strong correlations with full load hours. Both can explain the developments for VRE-high. For VRE-medium no significant relation with the recession was found and for VRE-low both factors were not significant. For energy efficiency, the commissioning year shows a strong correlation for natural gas and less for coal. Significant impacts are found for average commissioning year and full load hours on the energy efficiency of natural gas-fired power generation but not for coal. [ABSTRACT FROM AUTHOR]

At present, integrated energy system (IES) suffers high operating cost due to poor energy saving and low emission reduction and insufficient consumption of renewable energy though wind and solar energy production are abundant. Thus, a low-carbon economic operation model is proposed in this paper. This model combines the characteristics of flexible load on the demand side and operates jointly with combined heat and power (CHP), carbon capture system (CCS), and power to gas (P2G). It considers the combination of source-side scheduling and demand-side scheduling. Finally, the problem is solved using MATLAB/YALMIP to call GUROBI and the interior point method (IPOPT) based on Hessian matrix iteration. The results show that the reasonable scheduling of the flexible load on the demand side realizes the peak load shifting and reduces the peak load regulation pressure of the system. The operation mode of source-side CHP coupled with CCS and P2G improves the utilization of renewable energy and curtails the carbon emission of the system. The proposed model transaction cost and daily operating cost are less than the base case by 61.34% and 50.49%, respectively. • An optimization model of P2G, CCS, and CHP with the flexible load was developed. • Multi-energy coupling relationship in the integrated energy system was analyzed. • Different flexible power and heat load to smooth the load curve were considered. • The operation effects of IES under different operation modes were compared. [ABSTRACT FROM AUTHOR]

Buildings consume more than 70% of electricity in the U.S. In order to reduce building energy consumption, advanced building controls have been developed. However, most building controls are using physics-based models and lack of scalability. Recent development of data-driven control models could overcome this challenge and be automatically developed and implemented on large scale. The purpose of this study was to evaluate the effectiveness, robustness, and scalability of automatic and systematic data-driven predictive control (DDPC) for a large-scale real-world deployment. We first used collected data from 78 buildings in RTEM database to train deep neural network models. Then we applied the models to optimize the HVAC control for energy savings. We focused on over 1000 HVAC units in five different commonly used types, including air handling units, rooftop units, variable air volume systems, fan coil units, and unit ventilators. Next, we evaluated the energy-saving potential and the reduction of greenhouse gas emissions of the proposed method. We found that DDPC was robust and scalable in buildings, with an average energy saving of 65% and peak load reduction of 15% compared to current control systems. The average reduction of GHG emissions for CO 2 , CH 4 , and N 2 O was 15.18 kg, 5.76e-4 kg, and 5.48e-5 kg per m2 per year, respectively. New York State can benefit 11% reduction in carbon emission from DDPC in buildings. For scalability, we also identified and categorized the challenging conditions when DDPC may not work properly and summarized the lessons learned from large-scale DDPC deployment. [Display omitted] • Large-scale development of data-driven deep neural network models for 78 buildings. • Development and test of data-driven predictive control for over one thousand HVAC systems. • Energy conservation and reduction in greenhouse gas emissions by data-driven predictive control. • Verified scalability for deploying data-driven predictive control in a large number of buildings. [ABSTRACT FROM AUTHOR]

Increasing environmental concerns are driving an evolution of the energy system in which electric vehicles (EVs) play an important role. Still, as the EV number increases, the adverse impact of charging is observed more widely, especially at the low-voltage level where high EV concentrations cause various detrimental effects due to the coincidence between EV charging and residential peak load. However, if managed properly, EVs become flexible resources which can improve the system operation, making them an attractive asset for the distribution system operator. With the recent technology development, new forms of local EV support can be developed, provided that an appropriate regulatory framework is established. Whereas the technical value of such EV distribution grid services has already been proven, integrating them into the European regulatory context is not straightforward. In the context where active distribution grid management schemes are still to be developed, it is important to recognise the barriers for active EV involvement in the early stage of the development. This manuscript focuses on identifying these barriers from a technology and infrastructure perspective as well as from the regulatory and market aspect. Various policy recommendations are provided for the stakeholders involved in the EV value chain. [ABSTRACT FROM AUTHOR]

Radiative cooling allows to cool down surfaces in a renewable way. It can be combined with solar collection functionality to produce heat and cold in a single device known as Radiative Collector and Emitter (RCE). In this paper we propose to combine a RCE with compression heat pumps to improve its performance, producing both heat and cold. Two combined systems are studied: a peak load shifting configuration where radiative cooling and cold production for demand occur simultaneously, and a decoupled configuration with an intermediate tank. The research numerically simulates both systems in different cities and climates, and compares them to a reference system based on a conventional heat pump. The results reveal and improvement of the performance in both configurations: a greater performance of the heat pump is found in the intermediate tank configuration; but after considering the electrical consumptions of all components of the system, peak load shifting configurations exhibit the best performance in most of the cities. Denver, Lleida and Rome – cities with dry climate and moderate cooling requirements – are the top cities with the greatest improvement of the performance. In the solar heating mode, the facilities show an underutilization of the collection field. • Two combinations of radiative cooling and water-water heat pump are analyzed. • The two systems supply cooling and DHW demands in a single-family house. • Simulations of combined heat pump and radiative collector and emitter are performed. • An improvement of the COP of the heat pump in combined systems is obtained. • Electricity savings up to 33% in combined systems are achieved. [ABSTRACT FROM AUTHOR]

Due to growing environmental and economic constraints, countries are exploring renewable sources such as wind, solar, and fuel cells to save energy, and develop the use of dispersed generations. Thus, the use of electric vehicles (EVs) is on the rise. On a large scale, either of these technologies can have damaging effects on the electricity grid; however, with suitable consumption-side management and programming, technologies and energy storage resources can reduce these effects. Thus, energy management optimization has become an interesting topic of research. Accordingly, the effect of the integrated aggregation of PEVs to the grid for the charge/discharge process and the resulting grid instability, especially at load peak time, is the main challenge to the use of these vehicles. The contribution of this paper includes the presentation of a model for managing the coordinated and uncoordinated charging system of grid-connected EVs with wind power and photovoltaic power units as dispersed generation sources and dividing the EVs into 4 classes by considering the share of each in the grid and considering a random number of vehicles per class using the normal distribution function and implementing the incoordination in wind speed and solar irradiation. The proposed model uses a novel Reinforcement Learning (RL) based onDeep Q Network (DQN) algorithm to solve the multi-objective problem. In this model, the costs of annual energy losses and the operation of dispersed generation units are discussed in an integrated manner as the objective function. The simulation is performed on a 57-bus IEEE grid and the results show the efficiency and improved performance of the model. • Presentation of a model for managing coordinated and uncoordinated charging system. • Dividing the EVs into 4 classes by considering the share of each in the grid. • A novel RL-DQN algorithm is implemented to solve multi objective function. • Third objective function include operation, emission and loss cost are considered. [ABSTRACT FROM AUTHOR]

Chinese Modular High-Temperature Gas-Cooled Reactor HTR-PM is a promising energy source for comprehensive utilization. The original coordinated control strategy of HTR-PM is a "turbine following reactor" strategy, which is not able to perform load following operation. To have more flexible operation capability, this paper presents a load following control strategy for HTR-PM. A balance of plant (BOP) model of HTR-PM is first developed for dynamic simulation, including the once through steam generator (OTSG), turbine generator, condenser, feed water heater, deaerator, feed water pump, valves and piping system. Then the original coordinated control strategy and the proposed load following control strategy are clarified, and model validation, simulation results are documented respectively. For turbine trip without reactor shutdown from 100%PFP transient, the steam dump strategy is improved to meet the safety requirements. Results demonstrate that the proposed load following strategy can meet the requirements of safety operation, which is desirable for participation in peak load regulating of power system. • A load following control strategy for Chinese Modular High-Temperature Gas-Cooled Reactor HTR-PM is proposed. • HTR-PM balance of plant system is modeled and validated. • The proposed control strategy is verified by simulations of typical operation transients. • Steam dump system is improved to enhance system performance. [ABSTRACT FROM AUTHOR]

Demand Side Management, an integral feature of smart grid, exhibits high potential in load balancing and load shifting for efficient management of the power grids. Strategically designed DSM mechanisms can help mitigate load shedding and facilitate peak load management of grids. In India, only flat rate and inclining block rate tariff mechanism exist for residential customers. With the onset of 14 smart grid pilot projects in India, there is a need to address the realization of potential underlying various features of a smart grid. As India is a price sensitive market, time of use tariffs and other demand response programs are expected to potentially affect the consumer behavior in power consumption. In this paper, the usage pattern of residential customers, collected from a pilot project in India is analyzed for scaling the potential of DSM strategies. Simulations have been carried out for a price based mechanism as well as an incentive based mechanism to evaluate the underlying potential of DSM exclusively for residential consumers. The results of the simulation indicate that there is a definite scope for load balancing as well as financial savings. [ABSTRACT FROM AUTHOR]

The rapid growth of air conditioners (ACs) in developing regions exacerbates some of the issues present in their electricity systems. Direct Load Control strategies can manage peak loads for optimizing energy usage for users and utilities. When applied to air conditioning, these strategies can be used for Demand Side Management which has not been fully explored for developing regions. In this paper a novel Direct Load Control mechanism is proposed, wherein control is based on technical constraints (generation capacity). To determine the effects of the proposed mechanism, a case study of the Indian State of Karnataka is carried out. The results indicate 0.88% energy savings and a reduction of almost 2% in regional peak loads in a state where only around 5% of all households have AC units. The case study shows that an AC Direct Load Control can have significant economic and environmental impacts, and can help improve current energy, water and climate issues. Continuous improvements in both energy access and economic conditions of developing regions are leading to a larger number of AC's installed, meaning that a mechanism that enables sustainable AC consumption could be of great use for all the stakeholders in the developing countries' electricity sectors. • A novel Direct Load Control for residential air conditioning is proposed. • The control can be implemented in developing regions without price-based markets. • A case study of the novel control shows a summer demand reduction of 0.88%. • The reduction of the demand leads to environmental and economic benefits. • The proposed control reduces negative impacts of growing air conditioning demand. [ABSTRACT FROM AUTHOR]

This study investigated the performances of five different substation configurations in single-family houses supplied with ULTDH (ultra-low-temperature district heating). The temperature at the heat plant is 46 °C and around 40 °C at the substations. To avoid the proliferation of Legionella in the DHW (domestic hot water) and assure the comfortable temperature, all substations were installed with supplementary heating devices. Detailed measurements were taken in the substations, including the electricity demand of the supplementary heating devices. To compare the energy and economic performance of the substations, separate models were built based on standard assumptions. The relative heat and electricity delivered for preparing DHW were calculated. The results showed that substations with storage tanks and heat pumps have high relative electricity demand, which leads to higher integrated costs considering both heat and electricity for DHW preparation. The substations with in-line electric heaters have low relative electricity usage because very little heat is lost due to the instantaneous DHW preparation. Accordingly, the substations with in-line electric heaters would have the lowest energy cost for DHW preparation. To achieve optimal design and operation for the ULTDH substation, the electricity peak loads of the in-line electric heaters were analysed according to different DHW-heating strategies. [ABSTRACT FROM AUTHOR]

In this study, the extent to which different climate scenarios influence overheating risk, energy use and peak loads for space conditioning of district heated multi-storey buildings in Sweden are explored. Furthermore, the effectiveness of different overheating control measures and the implications of different electricity supply options for space cooling and ventilation are investigated. The analysis is based on buildings with different architectural and energy efficiency configurations including a prefab concrete-frame, a massive timber-frame and a light timber-frame building. Thermal performance of the buildings under low and high Representative Concentration Pathway climate scenarios for 2050–2059 and 2090–2099 are analysed and compared to that under historical climate of 1961–1990 and recent climate of 1996–2005. The study is based on a bottom-up methodology and includes detailed hour-by-hour energy balance and systems analyses. The results show significant changes in the buildings’ thermal performance under the future climate scenarios, relative to the historical and recent climates. Heating demand decreased significantly while cooling demand and overheating risk increased considerably with the future climate scenarios, for all buildings. In contrast to the cooling demand, the relative changes in heating demand of the buildings under the future climate scenarios are somewhat similar. The changes in the space conditioning demands and overheating risk vary for the buildings. Overheating risk was found to be slightly higher for the massive-frame building and slightly lower for the light-frame building. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of a price-based control system in conjunction with energy storage using phase change materials for two applications: space heating in buildings and domestic freezers. The freezer used for this experimental study was provided with energy storage trays containing a eutectic solution of ammonium chloride (melting point of −15 °C). In the building application, DuPont wallboards were used to provide thermal storage. Experimental results showed that using thermal storage material in conjunction with the proposed price-based control method can improve performance of these systems and lead to a successful peak load shifting. Depending on electricity price trends, cost savings using the proposed strategy can vary. Savings of up to 16.5% and 62.64% per day were achieved for the freezer and building applications respectively, based on New Zealand electricity rates. [ABSTRACT FROM AUTHOR]

In this paper, an optimal load scheduling model of household appliances for smart home energy management considering demand response for different pricing schemes is proposed. Henry gas solubility optimization method is used to obtain optimal load scheduling solutions, and then these solutions are sorted using VIKOR multi-criteria decision making method. Multiple pricing schemes have been used such adaptive consumption level pricing scheme and time of use. The use of multiple pricing scheme is to compare the results of customers cost saving obtained. The results show, the proposed method a saves more than 80% and 76% of end-user energy cost for time of use and adaptive consumption level pricing scheme respectively. Meanwhile, the peak load of end-user is reduced by 55% for the baseline operation. The outcomes of this paper are also compared with other works in the literature in order to validate the proposed method. It is concluded in this research that the proposed method provides essential saving for energy cost and demand's peak power as well as keeps acceptable range of customer inconvenience level. • We propose multi-objectives optimization method for cost and energy saving. • The solutions is evaluated by using hybrid multi-criteria decision making methods. • The provided solutions of end-users are subject to acceptable level of inconvenience. • The proposed method a saves more than 80% of end-user energy cost. • The proposed method reduces end-user's peak load by 55% for the baseline operation. [ABSTRACT FROM AUTHOR]

Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand. • A novel stochastic-interval model for robust scheduling of PVaCSs is developed. • A comprehensive stochastic model for multi-mode EV charging is presented. • A multi-stage solution procedure for the developed model is described. • The new proposal allows to consider optimistic and pessimistic strategies. • A case study validates the developed formulation and modelling. [ABSTRACT FROM AUTHOR]

Space heating accounts for a significant share of a building's energy use; moreover, occupants' desires for better thermal comfort may further increase the energy use and peak load of the space heating system, as well as its total energy cost. The goal of this study was to use an optimal control technique: model-based predictive controller, to improve the energy and economic performance of space heating systems with satisfying indoor temperature. The proposed optimal controller incorporated the dynamic energy prices, the disturbances from weather and occupancy, as well as a nonlinear system dynamic model that considered the building thermal mass as thermal energy storage. The model-based predictive controller was tested by simulation on a university building in central Norway, and its control performance was compared to a conventional rule-based control approach, which is currently employed by the case system. The model-based predictive controller's effectiveness was demonstrated by a 2.8% reduction in heat use, 8.8% shaving in peak load, 3.8% saving in heating cost, and a 59% drop in indoor temperature violation. Furthermore, a sensitivity study revealed that the model-based predictive controller still maintained high energy and economic performance even under lightweight building constructions with decreased building thermal mass. • Incorporate dynamic energy prices and a nonlinear system model. • Utilize building thermal mass as thermal energy storage. • Improve system performance by peak load shaving and heat use saving. • Improve occupants' thermal comfort by decreasing indoor temperature violations. • Sensitivity study of building thermal mass to draw generalized conclusions. [ABSTRACT FROM AUTHOR]

This study introduces the concept of reserve margin volatility to analyze factors influencing the fluctuation of reserve margin. This study defines reserve margin volatility as the percentage difference between the expected reserve margin and actual reserve margin. Internal and external factors increase reserve margin volatility, which can lead to regional or national blackouts and cause an economic waste problem. In this regard, this study derives significant variables affecting the reserve margin volatility of South Korea using robust regressions by season through heteroskedasticity and autocorrelation consistent estimators. Meteorological factors including temperature, humidity, heating and cooling degree days, holidays, peak load forecasting error, and the proportion of renewable energy are used as variables to identify the significant determinants. This study found that meteorological variables affect reserve margin volatility in summer and winter to a greater degree than in spring and autumn. Holiday variables decrease reserve margin volatility regardless of season. Mean humidity increases reserve margin volatility only in summer. In addition, peak load forecasting error and the proportion of renewable energy significantly increases reserve margin volatility regardless of season. • The concept of reserve margin, focusing on its volatility, is introduced. • Reserve margin volatility is defined as the gap between expected and actual reserve margins. • Multiple linear regression by season with robust estimator is conducted. • Peak load forecasting error significantly affects the reserve margin volatility. • Proportion of renewable energy increases the volatility regardless of season. [ABSTRACT FROM AUTHOR]

One of the principal purposes of using energy storage systems (ESS) are peak shaving and load smoothing. In the present study, a novel control algorithm has been developed in order to the optimal use of ESS for peak load shaving. First, by comparing the complex control algorithm of the present study with the control algorithm developed by other researchers (simple control algorithm), the superiority and advantage of the present work become clear. To do this, five different load demand profiles have been considered and the mentioned algorithms have been applied to them. The results show that the implementation of the present study novel algorithm on the load demand profiles number one to five reduces the maximum of the required grid power to 6.477%, 11.81%, 12.60%, 10.25%, and 3.000% of their initial value, respectively. Their variance also reduces their initial value to 14.47%, 14.20%, 28.48%, 39.87%, and 54.96%, respectively. On the other hand, the implementation of the simple control algorithm has reduced the maximum of the required grid power to 0.8542% of its initial value only in the fifth profile. But the variance in the first to fifth profiles is reduced by 9.630%, 5.831%, 14.12%, 21.89%, and 52.00% of their initial value, respectively. In the following, by applying the novel control algorithm on the first sample load demand profile and change of various parameters such as ESS capacity, lower and upper limitation of the grid power, etc. the results are extracted and reported in detail. [Display omitted] • A novel control algorithm & scheduling procedure was developed. • By applying the algorithm, optimal operation of energy storage systems can be extracted. • The proposed algorithm is appropriate for peak shaving and load smoothing purposes. • Different load profiles were used to show how the load was shaved optimally. • The developed algorithm can be used for any load profile as well as any energy storage system with different capacity. [ABSTRACT FROM AUTHOR]

This study uses an advanced airflow, energy and humidity modelling tool to evaluate the potential for residential mechanical pre-cooling of building thermal mass to shift electricity loads away from the peak electricity demand period. The focus of this study is residential buildings with low thermal mass, such as timber-frame houses typical to the US. Simulations were performed for homes in 12 US DOE climate zones. The results show that the effectiveness of mechanical pre-cooling is highly dependent on climate zone and the selected pre-cooling strategy. The expected energy trade-off between cooling peak energy savings and increased off-peak energy use is also shown. The best pre-cooling results (more than 75% energy use shifted away from peak while minimising the total energy penalty) for most climates were obtained using a medium (5 h) pre-cooling time window with a shallow (23.3 °C) pre-cooling set point temperature. All of the pre-cooling strategies investigated caused the annual cooling energy demand of the simulated buildings to increase. Additionally, all of the pre-cooling strategies shifted at least 50% of the on-peak cooling loads away from a peak period window of 4pm–8pm in all climate zones. [ABSTRACT FROM AUTHOR]

With the advancement of power system reform and the improvement of renewable energy penetration, the situation of power generation enterprises and the generation capacity adequacy of power system are facing challenges. It is urgent to ensure reliable power supply in the market through new evaluation methods and guarantee mechanisms. Firstly, this paper proposes an evaluation method of generation capacity adequacy for multi-types units, which quantitatively calculates the adequacy capacity of each unit and the generation capacity adequacy of the system. Secondly, this paper designs a unit capacity compensation mechanism adapted to the initial stage of Power Market construction, which includes capacity pricing model, compensation capacity quota model, capacity fee settlement model. Among them, the capacity pricing model considers the investment cost of marginal units operating during annual peak load, while the compensation capacity quota model considers the actual generation capacity of units and the actual demand of the system. Finally, this paper takes a certain region of China as an example to verify the effectiveness and adaptability of the mechanism. The results show that the capacity adequacy guarantee mechanism designed in this paper can effectively reflect the overall level of power system generation capacity adequacy, fairly distinguish the reliability value of various generation units, help power generation enterprises recover fixed costs within the planned investment payback period and ensure the system generation capacity adequacy. • A method for capacity adequacy evaluation of multi-types generator unit is proposed. • A unit generation capacity compensation mechanism proposed is proposed. • The implementation process of the compensation mechanism is proposed. • The impact of different load levels on the generator units fixed cost recovery by capacity compensation mechanism is analyzed. • The impact of different renewable energy penetration on system reliability and economy of system power supply is analyzed. [ABSTRACT FROM AUTHOR]

Decreasing distribution temperature is the key driving force for energy-efficient and economic-competitive district heating (DH) systems, powering the transition from the 1st generation high-temperature steam-based DH systems to the 4th generation low-temperature water-based DH systems. However, for heat prosumers integrated with thermal energy storages (TESs), the decreasing distribution temperature may lower the peak load shaving capability of TESs and raise the peak-load-related heating costs, due to decreased charging temperature. This study, therefore, aimed to investigate the overall impacts of distribution temperature and to identify the economic optimal distribution temperature for heat-prosumer-based DH systems integrated with short-term TES. Firstly, an economic optimization problem was developed. Afterwards, distribution temperatures were optimized within the feasible regions of individual scenarios, including three benchmark scenarios representing the 2nd, 3rd, and 4th generation DH and an improved scenario featured high feasibility in distribution temperature. A campus DH system in Norway was used as the case study. Results revealed that the improved scenario's economic optimal distribution temperature was significantly distinct from the 4th generation DH scenario's lowest distribution temperature, both in terms of distribution range and annual average level. Finally, broad conclusions were reached by discussing the impacts of crucial factors on economic optimal distribution temperatures. • Economic optimization considering heat use and peak load-related heating costs. • Optimize the yearly distribution temperatures. • Case study on a campus heat prosumer with data centre waste heat recovery. • Sensitivity studies on storage capacity and renewable fraction. • Higher supply temperature flexibility ensures better economic performance. [ABSTRACT FROM AUTHOR]

This paper presents a techno-economic optimization procedure for selecting the best energy mix of renewable energy sources to meet the predefined power demands of an isolated community. The simulation model, based on Trnsys software, incorporates the technical characteristics of each plant component, site meteorological data, investment, and operation and management costs. An optimization procedure selects the best combination and sizing of each generation system and energy storage capacity to achieve the minimum value of Levelized Cost of Electricity (LCOE) for different renewable energy fractions from 40% to 100%. A case study for a new renewable community with a peak load of 50 MW in the Tabuk region, Kingdom of Saudi Arabia (KSA), is presented and discussed. The implementation of a complete renewable system raised the LCOE trend to 0.156 USD/kWh from 0.081 USD/kWh. The optimal renewable penetration for the analyzed case is approximately 80%, with an overall LCOE of 0.092 USD/kWh. The optimized power mix highlights the possibility of providing clean energy without a huge cost burden. • High penetration of renewables in remote communities requires an optimized energy mix. • PV and wind coupled with CSP benefit from increased energy dispatchability. • Thermal energy storage enables a reduction in LCOE in energy mix with high renewables content. • The load-following operation requires the use of high storage capacity. • The LCOE is minimized by minimizing the use of electric storage at utility-scale. [ABSTRACT FROM AUTHOR]

Current residential price tariff structures in Australia, which are predominately based on a flat daily supply charge combined with a per kWh electricity charge, create a distortion to the electricity consumption pattern, leading to larger afternoon and evening peak demands across the networks. Battery storage connected to solar PV (photovoltaic) array would reduce these effects in the presence of alternative price tariffs that incentivise households to shift load and reduce the peak demand. This challenge is addressed using a choice-diffusion model to forecast PV and battery storage uptake to 2025 for a case study in Townsville, Australia. Sensitivity of uptake is tested for six different price tariffs based on flat, time-of-use and critical-peak-pricing. Uptake of battery storage connected to solar PV ranged between 3% and 5.4% of households at 2025, depending on the price tariff, with the larger PV/battery options being more popular. Percentage of households disconnecting from the grid at 2025 is in the order of one percent depending on the price tariff. A sensitivity analysis showed battery price was a major driver to uptake whilst typical financial subsidies to purchase price have a lower effect. [ABSTRACT FROM AUTHOR]

There is a lack of the capacity to respond to peak loads in most provincial power grids of eastern China and coastal regions. A CDA (central dispatching authority), which is usually a regional power grid, is responsible to dispatch its own plants and allocate power generation to multiple subordinate provincial power grids for responding to their peak loads simultaneously. Hence, this paper develops an optimization model to determine the quarter-hourly generation schedules allocated for provincial power grids under a CDA. To meet the need for peak shaving, the selected objective function in the model requires minimization of the variance of remaining load that is obtained by subtracting power generation from the original load of each provincial power grid. The objectives of multiple power grids are first reduced to an equivalent scalar objective through the weighted sum method. The general quadratic and linear formulations applying to the conventional plants and pumped-storage plants are developed to respectively deal with complex composite objective and spatial-temporal coupling constraints such as multilateral electricity contracts constraints and load balance constraints. The resulting problem is finally solved via the convex quadratic optimization technique. The proposed method is applied to scheduling plants owned by the East China Grid which covers five provinces. The simulations show that the method can rationally coordinate electric power among provinces to meet different peak demands. The comparison with the conventional method further demonstrates the advantages of our method. [ABSTRACT FROM AUTHOR]