新能源与储能的跨日多时间尺度调度（周、多日、季度、年度尺度）

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This paper proposes a pricing and scheduling method for shared mobile energy storage systems (SMSs) in coupled power distribution and transportation networks. Different from existing shared energy storage studies, which mostly focus on stationary resources, the paper investigates the SMS operation considering the negotiation of rental prices as well as mobility and charging/discharging among SMS owners and different users. Specifically, the SMS pricing and scheduling with variable renewable energy are established as a bilevel mixed-integer chance-constrained distributionally robust optimization problem. In the upper-level problem, the SMS owner determines pricing and day-ahead mobility strategy to maximize its payoff. In the lower-level problem, the SMS users, i.e., distribution grid operators, determine the SMS charging/discharging power according to the SMS day-ahead pricing results and intra-day distribution grid operation strategies for accommodating variable renewable energy. The distributionally robust chance constraint is designed to cope with the intra-day operational risk caused by the variability of renewable power generation. To cope with the solution difficulty in the proposed bilevel optimization problem, the chance constraint is reformulated as second-order cone constraints, which are further transformed into a set of linear constraints, and then the reformulated bilevel mixed-integer linear programming problem is decomposed and iteratively solved to avoid enumerating lower-level integer variables. Simulation results show that the utilization rate of SMS batteries is increased and the excess renewable power is fully consumed when SMSs are shared among different distribution grids. The proposed distributionally robust optimization achieves higher revenue for the SMS owner and smaller operating costs of distribution grids than robust optimization under uncertain environments.

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This paper presents a planning model that utilizes mobile energy storage systems (MESSs) for increasing the connectivity of renewable energy sources (RESs) and fast charging stations (FCSs) in distribution systems (DSs). The proposed planning model aims at enabling high penetration levels of green technologies while minimizing the total DS cost that includes investment, operating, and emission costs. The proposed model determines the optimal MESS sizes and transportation schedules as well as the optimal sizes and locations of wind-based distributed generators (DGs), photovoltaic (PV) DGs, and FCSs. The model takes into account techno-economic and environmental factors in addition to the power variations of RESs, FCSs, and load demands. The proposed planning model is applied on two benchmark test systems (i.e., 33-bus and 69-bus DSs). To evaluate the efficacy of the proposed model, the results obtained from the model are compared to those obtained from a traditional planning technique. The comparison of the results demonstrates that the proposed model with MESSs successfully achieves a significant cost saving and increases the penetration levels of RESs and FCSs.

Abstract The exploitation of renewable energy sources is crucial in promoting energy transition from fossil-based to renewable-based, but their intermittent nature causes the mismatch of energy supply and demand. This can be resolved using an energy storage to store the excess energy from renewables and release it when the energy supply is in deficit. Hydrogen has a good potential for energy storage as it can tackle the spatial differences in renewable energy supply, but its storage in compressed gas or cryogenic liquid form incurs high investment cost. Liquid organic hydrogen carrier offers a cheaper and more convenient way of storing hydrogen as compared to conventional means. This paper proposes a cascade analysis approach for the targeting and scheduling of a self-sufficient standalone energy system that is fully supported by renewables and with liquid organic hydrogen carrier as energy storage. The fraction of hydrogen and electricity consumed for the system self-requirement is first determined before cascade analysis is performed to identify the optimal capacity of the energy system. Results indicated that 255,020 m2 of solar panel is required for a region with 45.1 MWh daily electricity demand. If the system energy requirement is neglected, most equipment would be undersized by about 40%.

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As renewable energy sources such as wind energy replace traditional power plants, new methods of component sizing and energy management for hybrid storage systems are necessary to achieve the expected dispatched power level that is committed to supply to the grid for a specific time interval. Electrolyzers (ELs), fuel cells (FCs), and hydrogen storage tanks working as hydrogen energy storage (HES) can, not only offer electrification of the power sector but also offer flexible dispatch power level in the wind energy generation system. To gain the benefits of HES, this study proposes a probabilistic approach to adequately size a hybrid energy storage system composed of a proton exchange membrane fuel cell/electrolyzer, and a supercapacitor (SC) bank. Furthermore, a two-layer energy management to improve power dispatch scheduling for the HES is proposed. Using real-world wind data, the proposed size specification method was simulated and compared to other existing methods. The simulation results demonstrate that the SC within the hybrid energy storage system can aid in the processing of high-frequency fluctuations and avoid the substantial cost of round-trip losses associated with HES. Furthermore, the two layers energy management strategy assists in extending HES operating lifetime, reducing operation cost, and maximizing HES unit utilization by avoiding excessive number of switching between FCs and Els and maintaining an equal number of turning ON/OFF of ELs (charging) and FCs (discharging).

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In Part I of this paper we have introduced the closed-form conditions for guaranteeing regional frequency stability in a power system. Here we propose a methodology to represent these conditions in the form of linear constraints and demonstrate their applicability by implementing them in a generation-scheduling model. This model simultaneously optimises energy production and ancillary services for maintaining frequency stability in the event of a generation outage, by solving a frequency-secured Stochastic Unit Commitment (SUC). We consider the Great Britain system, characterised by two regions that create a non-uniform distribution of inertia: England in the South, where most of the load is located, and Scotland in the North, containing significant wind resources. Through several case studies, it is shown that inertia and frequency response cannot be considered as system-wide magnitudes in power systems that exhibit inter-area oscillations in frequency, as their location in a particular region is key to guarantee stability. In addition, securing against a medium-sized loss in the low-inertia region proves to cause significant wind curtailment, which could be alleviated through reinforced transmission corridors. In this context, the proposed constraints allow to find the optimal volume of ancillary services to be procured in each region.

Modern power systems are undergoing a low-carbon and sustainable transition. The increasing penetration of renewable energy sources (RESs) poses significant challenges to the power system scheduling due to the associated uncertainties. Moreover, the integration of various flexible elements further complicates the scheduling problem. Therefore, rapid and accurate real-time scheduling methods are required to ensure the safe and stable operation of the power system. In this paper, a hybrid approach of expert knowledge and reinforcement learning (RL) is proposed to solve the real-time scheduling problem of the high-penetrated renewable power system. Firstly, a mathematical model for real-time scheduling of the high-penetrated renewable power system including flexible loads and energy storages (ESs) that integrates system operating costs and constraints, and RESs consumption is established and formulated as a Markov decision process. Subsequently, the proposed approach introduces expert knowledge as an intermediary between the power system and the RL agent, utilizing the optimized unit control sequence derived from the RL algorithm for scheduling decisions. Case studies conducted on the SG 126-bus system validate the effectiveness of the proposed approach and demonstrate its tremendous potential to facilitate RES consumption.

Abstract Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) has received much attention in the recent years due to its merits of no fossil fuel consumption, low costs, fast start-up and wide-ranging part load ability. It is considered to have a variety of power gird applications including providing reserve services. Although a number of studies are reported in the optimal scheduling strategy of using compressed air energy storage, very few studies have been reported in AA-CAES reserve capacity modelling. This paper presents a reserve capacity model for an AA-CAES facility considering its working mode conversion process, the dynamic characteristics, the air pressure limitations, the thermal storage capacity limitations and the power output limitations of AA-CAES. The developed reserve capacity model is then used in the power system optimal joint energy and reserve scheduling. In the scheduling, the limits on the reserve capacities of Thermal Power Units (TUs) and Interruptible Loads (ILs), which are caused by AA-CAES, are taken into account. The developed scheduling model are used to analyse the impacts of AA-CAES on the system energy and reserve schedules, the system operation costs and the wind curtailment. Numerical simulation results indicate that the participation of AA-CAES in power system operation does not only reduce the system energy and reserve costs, but also mitigate the wind curtailment. However, it is found that AA-CAES is unsuitable for undertaking the system reserve demand alone and using AA-CAES to provide reserve services may increase the system total reserve demand.

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To cope with the challenges of multi-timescale coordination of energy storage and coupled scheduling of multiple energy with a high proportion of renewable energy penetration, this paper proposes a multi-timescale hierarchical rolling scheduling framework for electric-hydrogen-ammonia integrated energy system (EHA-IES). In the upper layer, a fuzzy forecast method based on climate weights fusion generates annual renewable energy generation to drive yearly ammonia scheduling. In the middle layer, after receiving decisions from the upper layer, information gap decision theory (IGDT) is applied for weekly hydrogen scheduling, which balances the robustness and economy of hydrogen scheduling. In the lower layer, with the upper and middle layers transferring the results as boundary relaxations, daily electric scheduling is achieved by model predictive control (MPC). Then, the feedback mechanism of rolling dynamic corrections is proposed to re-execute hydrogen and ammonia scheduling decisions. The simulation analysis demonstrates that the hierarchical rolling scheduling framework facilitates multi-timescale energy transfer, enhancing the system’s robustness in response to fluctuations in renewable energy. Furthermore, the total economic cost of full-period multiple energy operation decreases by 7.30% and 14.28% compared to the electric-hydrogen hierarchical scheduling and the electric-ammonia hierarchical scheduling, respectively.

The paper aims at demonstrating that the consideration of constant start-up costs and ramps of the thermal generating units for assessing the contribution of pumped-hydro energy storage to reduce the scheduling costs of hydrothermal power systems with high wind penetration, may yield unrealistic results. For this purpose, an isolated power system is used as a case study. The contribution of a pumped-storage hydropower plant to reduce the system scheduling costs is assessed in the paper by using a hydrothermal weekly unit commitment model. The model considers different start-up costs and ramps of the thermal generating units as a function of the start-up type. The effects of including pumped hydro energy storage in the system on the integration of wind energy, and on the start-ups and capacity factors of the thermal generating units are also evaluated. The results of the paper demonstrate that the consideration of constant start-up costs and ramps of the thermal generating units yields unrealistic results, and that the pumped-storage hydropower plant may help reduce the system scheduling costs by 2.5–11% and integrate wind power and may allow dispensing with some inflexible thermal generating units.

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A new method for the optimization of seasonal energy storage is presented and applied in a case study. The optimization method uses an interval halving approach to solve computationally demanding mixed integer linear programming (MILP) problems with both integer and non-integer operation variables (variables that vary from time step to time step in during energy storage system operation). The seasonal energy storage in the case study uses a reversible solid oxide cell (RSOC) to convert electricity generated by solar photovoltaic (PV) panels into hydrogen gas and to convert hydrogen gas back to electricity while also generating some heat. Both the case study results and the optimization method accuracy are examined and discussed in the paper. In the case study, the operation of the RSOC and hydrogen storage system is compared with the operation of a reference system without energy storage. The results of the study show that installing an RSOC and hydrogen storage system could increase the utilization of onsite renewable energy generation significantly. Overall, the optimization method presents a relatively accurate solution to the case study optimization problem and a sensibility analysis shows a clear and logical pattern.

Hydrogen is characterized by zero carbon emissions and high energy density, which can effectively support the consumption of a high proportion of intermittent new energy. Considering the seasonal nature of renewable energy sources, a seasonal hydrogen storage model is incorporated in an electric-hydrogen integrated energy system (EH-IES). In this paper, a two-layer optimization method is proposed for EH-IES with seasonal hydrogen storage. The problem of co-optimizing the equipment capacity and configuration in the proposed system is coordinated by establishing a two-layer optimization framework. Specifically, the system is optimized to minimize cost and carbon emissions at the upper layer using the multi-objective stochastic paint optimizer (MOSPO) algorithm, with the capacity configuration results being transmitted to the lower layer. The lower layer, aiming to reduce the total system cost, utilizes a commercial solver to obtain the optimal economic scheduling results for a typical day. The final analysis of the four scenarios shows that the increase in renewable energy reduces the purchasing cost of electricity by 1.89%, while in contrast, the total cost increases by 4.4% in the system with a lower proportion of renewable energy. In the case of higher heating and cooling loads, the increase in renewables reduces the purchase cost of natural gas by 9.10%. The results demonstrate that the proposed method can leverage the seasonal complementary benefits to drive new energy consumption, enhance system operation efficiency, and effectively reduce EH-IES’s total operation cost and carbon emission.

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Residential and small commercial users face growing challenges with the variability in photovoltaic (PV) power generation and extreme load fluctuations, influenced by factors like building thermal conditions, the number of occupants inside buildings, and changing weather. A shared energy storage systems behind the smart meters present a proactive solution, offering these users enhanced flexibility to optimize their energy usage. In this paper, cloud energy storage architecture is managed under the user's building thermal comfort and PV power generation uncertainty scenario. A hardware module is developed using ESP32 microcontroller and PZEM004 T meter components to collect energy consumption data. The RS-485 communication interface protocol is used in the hardware module. A data-driven net demand error forecast-based strategy has also been developed to minimize the PV power and load uncertainty effect, including outdoor temperature. The particle swarm optimization algorithm optimizes the human thermal comfort set point. The price-based scheduling strategy is used to maximize user utilization. The numerical results with Indian grid price under uncertainty show that CES architecture service is more economical for users than grid-connected supply.

Summary The evident seasonal variations in photovoltaic output as well as electric and thermal loads will result in significant energy wastage and carbon emissions. In order to address the problem, a two-stage sizing cooptimization method considering economy-safety characteristics is proposed for the integrated energy system combined power-hydrogen-heat cogeneration (CPHH-IES), with seasonal hydrogen storage. Subsequently, an economic-durability-safety optimized objective is introduced, assessing the total cost throughout the sizing cycle, equipment degradation during operation, and safety indicators of the hydrogen energy system. Finally, a two-stage sizing framework based on heat-determined hydrogen is established, and a combined configuration-scheduling double-layer strategy is put forward within the framework to accommodate seasonal hydrogen storage and multi-energy coupling. The feasibility of the method was validated using data from a site in northwest China, demonstrating its capacity to ensure the safety of the hydrogen energy system and enable seasonal hydrogen storage.

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The current study concentrates on the planning (sitting and sizing) of a renewable integrated energy system that incorporates power-to-hydrogen (P2H) and hydrogen-to-power (H2P) technologies within an active distribution network. This is expressed in the form of an optimization model, in which the objective function is to reduce the annual costs of construction and maintenance of integrated energy systems. The model takes into account the planning and operation model of wind, solar, and bio-waste resources, as well as hydrogen storage (a combination of P2H, H2P, and hydrogen tank), and the optimal power flow constraints of the distribution network. Electrical and hydrogen energy are administered in an integrated energy system. The modeling of the uncertainties regarding the quantity of load and renewable resources is achieved through stochastic optimization using the Unscented Transformation method. The novelties of the scheme include the sizing and placement of a combined hydrogen and power-based renewable integrated energy system, the consideration of the impacts of bio-waste units, P2H, and H2P systems on the planning of the integrated energy system and the operation of the active distribution network, and the modeling of uncertainties using the Unscented Transformation method to reduce the calculation time. The study’s results demonstrate the scheme’s ability to improve the technical conditions of the distribution network by considering the optimal planning of integrated energy systems. In comparison to the network power flow, the operation status of the network has been improved by approximately 23-45% through the optimal siting, sizing, and energy management of hydrogen storage equipment, as well as renewable resources in the form of integrated energy systems. In other words, optimal energy management and planning of the integrated energy systems in the distribution network has been able to reduce energy losses and voltage drop by 44.5% and 42.4% compared to the load flow studies. In this situation, peak load carrying capability has increased by about 23.7%. In addition, compared to the case of the network with renewable resources, the overvoltage has decreased by about 43.5%. Also, Unscented Transformation method has a lower calculation time than scenario-based stochastic optimization.

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Data centers (DCs) are energy consumers with high electricity demand. Due to their Spatio-temporal demand response (DR) capabilities, DCs are crucial DR participants. In view of the difference in real-time requirements and continuity requirements of computing jobs, this work builds a detailed DR model of DCs. To make full use of the latest wind power predictive information, a multi-time scale optimal dispatch based on model predictive control (MPC) is proposed for wind power accommodation improvement and system operating security enhancement. As the real-time optimal dispatch is a quadratic programming problem and the total number of dispatching periods is large, the BP neural network is applied in this work to improve the computation speed. Finally, the proposed model is tested on an IEEE 30-bus power system with wind farms and DCs. Simulation results verify that DCs’ DR participation plays an important role in promoting wind power accommodation and system load adjustment. Besides, it is proven that our proposed BP neural network-based MPC method can obtain optimal dispatching results with low computation costs.

Abstract Hydropower system is a crucial support for the integration of various renewable energy sources. The integration of dispatchable hydropower and non-dispatchable photovoltaic (PV) power is promising to achieve efficient resource use. This paper proposes a coordinated optimization framework for the long-term complementary operation of large-scale hydro-PV hybrid systems. A multi-objective optimization model is established that simultaneously optimizes the economic benefit and operational safety of the hybrid system, i.e., the quantity and quality of the joint power output. The proposed model decouples hydropower and PV power in time scales to maintain calculation accuracy and reduce problem dimensions. A parallel generic front modeling-based multi-objective evolutionary algorithm (GFM-MOEA) is designed to produce a well-converged and well-distributed set of Pareto optimal solutions. Also, we develop a novel robust decision-making model to evaluate, rank and select the Pareto optimal solutions, which allows potential uncertainties in input data to be considered. The proposed framework is applied to the Longyangxia hydro-PV hybrid power system, which is the largest hydro-PV power plant in the world. Several numerical experiments are conducted to examine the hydrological effect on multi-objective optimization as well as the effect of uncertainty levels on robust decision-making. The results show that: (1) a clear competing relationship exists between total generated power and stability of the joint power output; (2) hydropower can compensate for the PV power, mainly when the solar radiation is limited while the abundant water resource is available due to rainfalls; (3) hydrological regimes have significant impacts on the multi-objective optimization results and the complementary effect; (4) the robust decision-making model enhances the reliability of the risk-informed complementary operation strategy by measuring the robustness and uncertainty of the decision.

The high penetration of renewable energy is becoming an important feature of new power systems. However, the power grid is facing greater threats of failures with the increasing frequency of extreme weather, making it necessary to enhance the resilience of power systems. In this paper, a multi-time-scale energy storage planning system is proposed for power system resilience improvement. Firstly, the characteristics of multi-time-scale energy storage are analyzed, and models of battery energy storage and hydrogen energy storage are established. Secondly, based on an analysis of random extreme weather scenarios, a bi-level stochastic programming model for multi-energy storage aimed at enhancing the resilience of power systems is constructed. Finally, based on the modified IEEE-24 node system, the model solution and example analysis are carried out, and the optimal configuration scheme for multi-energy storage is obtained. The results show that multi-energy storage is able to adjust more flexibly and effectively improve the resilience of the power system. Compared with the configurations of short-term and long-term energy storage systems, adopting multi-timescale energy storage reduces the total cost by 22.77% and 14.08%, respectively, and improves resilience by 4.33% and 0.67%, respectively.

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The growing presence of renewable energy sources (RES), energy storage systems (ESSs) and flexible loads (FLs) in power systems necessitates a new approach to N-1 security in day-ahead operation planning considering stochasticity and time coupling. To this end, a comprehensive approach based on stochastic multi-period AC security-constrained optimal power flow (S-MP-AC-SCOPF) was proposed recently in a form of a large-scale non-linear programming problem, which is not scalable. This paper proposes the first-time new tractable solution methodology for the most complete AC-SCOPF problem to date: S-MP-AC-SCOPF. As other main novelty, the proposed methodology achieves tractability by solving sequentially a limited number of different linear approximations of the full S-MP-AC-SCOPF problem. These linear approximations differ in terms of: state dependency regarding losses approximation, carefully reduced sets of constraints or tightening of critical constraints. The performance of the methodology is demonstrated in power system models of Sweden and Portugal. Extensive numerical experiments have shown that the methodology is able to reduce progressively until complete removal the number of violated thermal or voltage constraints. It leads to reasonably accurate solution with small optimality gap within few iterations and is at least 85% faster than the competitor IPOPT solver.

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This paper proposes a novel three-stage planning model for an integrated electricity and heat system (IEHS) with seasonal thermal energy storage (STES) and short-term TES, which considers the different energy cycling characteristics of STES and short-term TES and coordinately addresses multiscale uncertainties. In the proposed model, heat demand is firstly decomposed into two components reflecting seasonal and daily heat demand variations. Then, the operation of STES and short-term TES is separated considering multiscale uncertainties, where the large-scale uncertainty of seasonal heat demand is modeled as a fuzzy set in the second stage whereas the small-scale uncertainty of daily heat demand and wind power is represented by a set of scenarios in the third stage. To improve the computational efficiency while preserving the chronological continuity of the state of charge (SoC), a new STES model with the SoC limits only at the initial point in each representative period is developed. Furthermore, a pairwise reformulation is proposed to linearize the bilinear terms in pair fashion by taking advantage of the complementarity of two binary variables, which leads to fewer constraints with binary variables. Finally, numerical results demonstrate the effectiveness of the proposed planning model in improving the cost efficiency of the IEHS while reducing wind power curtailments. The superiority of the proposed STES model and pairwise reformulation on the improvement of computational efficiency is also verified.

To effectively supply the multi-energy loads and achieve the annual zero energy targets of zero energy buildings (ZEBs) throughout the planning horizon, this paper proposes a tri-level multi-energy system planning method for ZEBs considering both long- and short-term uncertainties. In the upper level, the optimal goal is to obtain an optimal device sizing scheme within the electric-thermal-hydrogen integrated multi-energy system (EHT-MES). The middle and lower levels tackle the long-term temperature change and short-term source-load uncertainties separately. For the former, a set of representative scenarios that include typical and extreme weather conditions are generated from the future temperature forecast dataset, and an ambiguous set is utilized to model the uncertain probability distributions of the scenario set. For the latter, to guarantee the reliable and economic operation of ZEBs under different seasonal-daily patterns, a hybrid stochastic and robust optimization (HSRO) method is applied to deal with short-term uncertainties from solar radiation, wind output, outdoor temperature, electric loads, and hot water loads. A reformulation method is proposed to transform the multi-level coupling planning model into an equivalent and tractable form, and an improved column-and-constraint generation (C&CG) algorithm is developed to solve the recast model. Simulation results verify the effectiveness of the proposed planning method in deploying ZEBs’ multi-energy devices and immunizing against multiple timescale uncertainties.

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Short-term energy storage systems, e.g., batteries, are becoming one promising option to deal with flexibility requirements in power systems due to the accommodation of renewable energy sources. Previous works using medium- and long-term planning tools have modeled the interaction between short-term energy storage systems and seasonal storage (e.g., hydro reservoirs) but despite these developments, opportunity costs considering the impact of short-term energy storage systems in stochastic hydrothermal dispatch models have not been analyzed. This paper proposes a novel formulation to include short-term energy storage systems operational decisions in a stochastic hydrothermal dispatch model, which is based on a Linked Representative Periods approach. The Linked Representative Periods approach disposes of both intra- and inter-period storage constraints, which in turn allow to adequately represent both short- and long-term storage at the same time. Apart from the novelty of the model formulation itself, one of the main contributions of this research stems from the underlying economic information that can be extracted from the dual variables of the intra- and inter-period constraints, which allows to derive an hourly opportunity cost of storage. Such a detailed hourly economic value of storage has not been proposed before in the literature and is not possible in a classic Load Duration Curve model that does not adequately capture short-term operation. This advantage is reflected in the case study results. For instance, the model proposed in this paper and based on Linked Representative Periods obtains operating decisions of short-term energy storage systems with errors between 5% and 10%, while the classic Load Duration Curve approach fails by an error greater than 100%. Moreover, the Load Duration Curve model cannot determine opportunity costs on an hourly basis and underestimates these opportunity costs of hydro (also known as water value) by 6%–24% for seasonal hydro reservoirs. The proposed Linked Representative Periods model produces an error on the opportunity cost of hydro units lower than 3%. Hourly opportunity costs for short-term battery energy storage systems using dual variables from both intra- and inter-period storage balance equations in the proposed model are also presented and analyzed. The case study shows that the proposed approach successfully internalizes both short- and long-term opportunity costs of energy storage systems. These results are useful for planning and policy analysis, as well as for bidding strategies of ESS owners in day-ahead markets and not taking them into account may lead to infeasible operation or to suboptimal planning.