Weekly-scale optimized complementary scheduling for a provincial power grid supplied by multiple cooperative pumped-storage power plants

The success of the energy transition from fossil fuels to renewable energies depends significantly on storage capacities. The decommissioning of coal-fired power plants in Germany over the next ten years will lead to a supply deficit of 30 to 40 GW at night without wind power. On sunny days, however, a surplus of photovoltaic electricity of over 50 GW is already expected and will continue to be expected in the near future. The traditional technology for storing large amounts of electricity is pumped storage. This robust technology has a service life of approximately 100 years, compared to batteries with approximately 20 years. However, there are very few available and approved sites for new pumped storage power plants in Germany. Therefore, alternative locations must be found. This article describes a new approach that proposes using existing, old open-cast lignite mines as sites for new pumped storage power plants. In the Renish mining district – a region between Cologne, Erkelenz, and Aachen – three large open-cast lignite mines are currently in operation, but these will be decommissioned in the coming years. The open-cast lignite mines reach depths of between 200 and 400 meters, thus offering ideal conditions for pumped-storage power plants. The Hambach mine, in particular, with its 400-meter depth, is exceptionally well-suited for such an unconventional pumped-storage power plant. A key feature is the separation of the water volume between the upper and lower storage basins from the residual water in the retention basins. The lower storage basin can be constructed in large, reinforced concrete caverns with a diameter of over 60 meters. The upper storage basin is located near the former ground level. The Hambach site allows for the installation of a capacity exceeding 6 GW and a storage capacity of over 50 GWh, which would surpass the maximum output of the largest pumped-storage power plant in Germany by at least five times. This Hambach pumped-storage power plant could cover a large portion of Germany’s total storage demand during the day-night cycle.

This paper proposes a distributed scheduling model for virtual power plants, and in order to realize the optimal scheduling of multiple virtual power plants, it proposes a reinforcement learning model and establishes a framework of reinforcement learning methods to subdivide the states, actions, and rewards that are generated by the grid scheduling center when it interacts with the power grid. An environment model including multiple virtual power plant operation models is constructed, and the objective function of the control center is set to allocate scheduling commands in the virtual power plants. The algorithmic analysis is based on the actual network framework of the regional power grid and divides the tariffs of power purchased/sold by the VPP to the power market. The centralized optimization method, distributed optimization method, and reinforcement learning optimization method are proposed respectively to solve the MVPP coordinated optimal dispatch model. The optimization objective, deviation control strategy, and carbon trading elements are exemplified, and different scenario scenarios are set to analyze the convergence of the reinforcement learning model (DDPG) and the scheduling results. Reinforcement learning real-time optimal scheduling focuses on the measured values of wind power and loads and avoids the increase of costs or decrease of revenues due to the fluctuation smoothing by the grid by coordinating the internal resources for smoothing or the complementary consumption through the inter-rate of VPP. It can take into account the impact of the current decision on future time, thus achieving scheduling optimization in multiple periods.

The bulk hydropower transmission via trans-provincial and trans-regional power networks in China provides great operational flexibility to dispatch power resources between multiple power grids. This is very beneficial to alleviate the tremendous peak load pressure of most provincial power grids. This study places the focus on peak operations of cascaded hydropower plants serving multiple provinces under a regional connected AC/DC network. The objective is to respond to peak loads of multiple provincial power grids simultaneously. A two-stage search method is developed for this problem. In the first stage, a load reconstruction strategy is proposed to combine multiple load curves of power grids into a total load curve. The purpose is to deal with different load features in load magnitudes, peaks and valleys. A mutative-scale optimization method is then used to determine the generation schedules of hydropower plants. In the second stage, an exterior point search method is established to allocate the generation among multiple receiving power grids. This method produces an initial solution using the load shedding algorithm, and further improves it by iteratively coordinating the generation among different power grids. The proposed method was implemented to the operations of cascaded hydropower plants on Xin-Fu River and another on Hongshui River. The optimization results in two cases satisfied the peak demands of receiving provincial power grids. Moreover, the maximum load difference between peak and valley decreased 12.67% and 11.32% in Shanghai Power Grid (SHPG) and Zhejiang Power Grid (ZJPG), exceeding by 4.85% and 6.72% those of the current operational method, respectively. The advantage of the proposed method in alleviating peak-shaving pressure is demonstrated.

The present-day world is starved of energy, and the requirements for energy are ever-increasing with the increase in population and the rise in standard of living of the people in the Asian region, where approximately 60% of the world’s population is located. To overcome this problem of energy requirements and also to reduce greenhouse gas emissions, the world has looked at renewable energies, in particular solar and wind, as an alternate sources. According to the Paris Agreement, India is committed to reduce its greenhouse emissions by intensity of its GDP by 33–35% over 2005, and it is on its track to achieve its target of 175 GW of renewable energy by 2022. The main composition of this is coming from wind and solar, which are highly intermittent in nature and present huge challenges for its integration into the grid. Flexible energy generation assets that have a capability to supply both base load and peaking power efficiently and economically are the need of the future and the necessary solution to address the dynamic, evolving energy needs of India. This can be achieved in the following ways: High renewable energy generation should be done and integrated with minimum conventional energy generations. Establishing pumped hydro plants and optimizing their usage to suit flexibility needs instead of traditionally using them as a peak/off-peak resource. Establishing large battery storage. Wind–solar–storage hybrid projects present a viable solution to the problem at hand and also for the future, wherein large renewable energy capacities are being planned to be added to the national grid. Developing such integrated projects in wind- and solar-resource-rich locations along with stand-alone pumped storage capacities independently, without impacting the existing natural water systems/irrigation systems, is necessary to sustainably power the future needs of India while maintaining grid stability. Rayalaseema region of Andhra Pradesh (AP) State in India is endowed with vast potential for the establishment of solar and wind power plants; nearly 3,000 MW of energy are being generated at present from these plants. This region is also known for its large network of reservoirs (on Krishna, Tungabhadra, Penna, Kundu, Hundri Rivers, and so on) and irrigational canals running through hundreds of kilometers. This network is underutilized, and the harvested energy from this is very poor. There is a large scope for the establishment of run of river like plants and pumped storage plants in this region. At present, AP government is planning to establish 10,000 MW of renewable energy, mostly from solar. With the introduction of this large intermittent nature of power, there is a need for the establishment of pumped storage plants and battery energy storage systems for consolidation and stabilization of power supply systems. This chapter will explore the possibilities of integration to the grid with solar, wind, and pumped storage along with the requirement of battery energy storage systems. The study will also identify the place of erection of pumped storage plants such that all the three components (solar, wind, and pumped storage) will be in close vicinity of each other. The power generated from each component will be pooled into a common substation for interconnection with the grid.

Large hydropower plants in China are usually required to transmit power to multiple provinces and meet their complex peak-shaving demands. How to model and solve this problem is challenging. Based on a Hydropower Project in southwest China, this study proposed a method for day-head operations of large hydropower plant considering peak-shaving demands of two provincial power grids. A short-term peak-shaving model for multiple power grids is constructed, which takes the minimum sum of the squares of the remaining loads of each power grid. This model considers complex constraints such as the operation of hydropower plants and the requirements of power transmission. The multi-objective optimization is transformed into a single-objective problem and solved by a group search algorithm based on the genetic algorithm. Two cases which respectively consider different loads and objective weights are presented to test the model. It is found that the large hydropower plant can simultaneously respond to different peak loads for multiple power grids. It should be also noted that different objective weights may have a significant effect on peak-shaving results.

A multi-objective short term hydropower scheduling model for peak shaving

As the integration of distributed energy resources (DERs) accelerates, optimizing active distribution networks (ADNs) for low-carbon operations has become essential for decarbonizing power systems. This paper proposes a bi-level optimization model aimed at achieving low-carbon scheduling of ADNs interacting with multiple technical virtual power plants (TVPPs) in the integrated electricity and carbon markets. The upper-level problem seeks to minimize ADN costs, including expenses associated with energy procurement and carbon emissions. The lower-level problem maximizes VPP economic benefits by optimizing their power generation, demand response, and trading strategies. To enhance carbon accountability, the dynamic emission factors (DEFs) are developed, encompassing both generation-based and trading-based emission factors. The generation-based DEF quantifies emissions based on the output of distributed generators, while the trading-based DEF allocates emissions for ADNVPP power exchanges, preventing double-counting and ensuring accurate carbon cost assessments. The bi-level model is then reformulated as a single-level mixed-integer linear programming (MILP) problem using Karush–Kuhn–Tucker (KKT) optimality conditions, Fortuny-Amat transformation, McCormick Envelop method, and the strong duality theorem. Case studies conducted on a modified IEEE 33-bus test system demonstrate that the proposed approach can effectively reduce carbon emissions by 7.93% and further reduce total operational cost considering carbon price, achieving both economic and environmental objectives. These findings underscore the potential of integrating low-carbon strategies into ADN management for more sustainable and resilient power systems.

The emergence of the shared energy storage mode provides a solution for promoting renewable energy utilization. However, how establishing a multi‐agent optimal operation model in dealing with benefit distribution under the shared energy storage is still a challenge. Considering the multi‐agent integrated virtual power plant (VPP) taking part in the electricity market, an energy trading model based on the sharing mechanism is proposed to explore the effect of the shared energy storage on multiple virtual power plants (MVPPs). To analyse the relationship among MVPPs in the shared energy storage system (SESS), a game‐theoretic method is introduced to simulate the bidding behaviour of VPP. Furthermore, the benefit distribution problem of the virtual power plant operator (VPPO) is formulated based on the Nash bargaining theory. In the case study, the proposed method is conducted in four VPPs with different resource endowments in terms of techno‐economic and operation efficiency. Results verify that the multiple virtual power plants with a shared energy storage system interconnection system based on the sharing mechanism not only can achieve a win‐win situation between the VPPO and the SESS on an operation cost but also obtain the optimal allocation scheme and improves the operation efficiency of the VPPs.

To effectively investigate the structural discrepancies and complementary energy characteristics among multiple virtual power plants (VPPs), and to improve the economic efficiency, low‐carbon performance, and operational reliability of the multi‐agent system, this paper proposes a low‐carbon collaborative optimal operation strategy for multiple VPPs based on the asymmetric Nash bargaining solution. First, considering the interests of various VPP operators and users, a cooperative operation framework for multiple VPPs under the green certificate and carbon trading mechanism is constructed using Nash bargaining theory, which facilitates electric–thermal energy transactions among different VPPs. Second, the Latin hypercube scenario generation method and a fast backward reduction technique are employed to handle the uncertainties associated with renewable generation and load power. Third, accounting for the multidimensional contributions of each VPP, an asymmetric Nash bargaining mechanism based on energy contribution rates is designed to allocate the cooperative gains among VPPs. Finally, the alternating direction method of multipliers (ADMM) is adopted to protect the information privacy of each participant. Case study results demonstrate that the proposed strategy can enhance the overall operational benefits and carbon reduction performance of the system, while promoting stronger collaboration and fairness‐oriented benefit allocation among VPPs.

Given the lack of distributed PV power generation operation and management capability, this paper profoundly analyzes the current situation of the application of big data technology in the process and management of PV power generation in China and proves the necessity and importance of promoting new equipment, new technology, and new mode based on traditional power system operation and management, using the extensive data information formed in the process and management of power plants, meteorology, environment, and power grid, and launching ample data analysis research. In addition, the typical application scenarios of big data technology in the operation and management of distributed PV power generation are explored, and a new mode of operation and management of distributed PV power generation at the power plant, grid, and government levels is explored and constructed. The study shows that with the dramatic increase in the number of distributed PV power generation, the use of big data technology in scenarios such as the power generation side and grid side has excellent potential to provide adequate support for PV power generation operation, grid scheduling, market-based transaction settlement, and government decision-making.

Influence and multi-objective optimization on three-stage guide vane closure scheme of a pumped storage power plant

This paper presents a dynamic simulation model of a laboratory-scale pumped-storage power plant (PSPP) operating in pumping mode with variable speed. The model considers the dynamic behavior of the conduits by means of an elastic water column approach, and synthetically generates both pressure and torque pulsations that reproduce the operation of the hydraulic machine in its instability region. The pressure and torque pulsations are generated each from a different set of sinusoidal functions. These functions were calibrated from the results of a CFD model, which was in turn validated from experimental data. Simulation model results match the numerical results of the CFD model with reasonable accuracy. The pump-turbine model (the functions used to generate pressure and torque pulsations inclusive) was up-scaled by hydraulic similarity according to the design parameters of a real PSPP and included in a dynamic simulation model of the said PSPP. Preliminary conclusions on the impact of unstable operation conditions on the penstock fatigue were obtained by means of a Monte Carlo simulation-based fatigue analysis.

The article provides information on the worldwide development of decentralized energy systems which are created on the basis of utilizing, distributing and storing renewable energy, using smart control systems, about energy storage in various energy storage systems, including pumped storage power plants considered one of the most efficient energy storage units. The map of a decentralized power system, based on a wind energy, a photovoltaic, and a pumped storage plant (PSP) is proposed; the methods of making estimates to determine its parameters are given. The article also provides the results of simplified estimates made to determine the main parameters of a 5 MW decentralized power system based on a wind power and a PSP, which prove the feasibility and efficiency of the proposed map.

The Pumped Hydro Storage Plants and Their Civil Engineering Structures

A new approach based on mixed-integer programming is proposed for daily economical operation planning problem for a multi-area system with hydro-thermal plants and a pump storage plant. All generator units can be coordinately and economically operated in order to gain the most economical effects of the whole power network. The calculation results also show that the approach proposed can successfully make use of pump storage plant to shift the peak of the load curve so that the operation condition of the whole power system can be greatly improved.