Net Load Research Articles

Net load forecasting method in distribution grid planning based on LSTM network

Distribution grid planning involves multiple nodes, lines, equipment, and other elements. Due to the large scale of the system, there are complex interactions in space. The net load is affected by the load changes of different nodes. If this spatial complexity is not fully considered, the net load prediction results may be inaccurate. Therefore, in order to ensure the effect of net load forecasting, a method of net load forecasting in distribution grid planning based on the Long Short-Term Memory (LSTM) network is proposed. This method fully considers the characteristics of distribution grid planning and constructs a net load forecasting model for distribution grid planning based on the LSTM network. This model selects the 3σ criterion detects and corrects the singular values in the historical load data, and obtains the reasonable maximum time series results of each day; The adaptive noise complete set empirical mode decomposition method is used to decompose the sequence results and generates Intrinsic Mode Function (IMF) components of each time series; According to the component results, a load forecasting model based on LSTM network is constructed, and the initial learning rate and cell number parameters of LSTM network are optimized by improving the Pelican optimization algorithm to improve the precision of load forecasting of LSTM network. The test results show that the method can detect singular values in the data and weaken the impact of grid planning on netload forecasting; It can effectively complete the decomposition of historical load data, and each component after decomposition will not be aliased; The prediction error of net load is less than 1.25%, which can provide a reliable basis for grid planning of distribution network.

Hierarchical Uncertainty Characterization of Monthly Net Load in Renewable Power Systems

Hierarchical Uncertainty Characterization of Monthly Net Load in Renewable Power Systems

Analysis on flexibility resources of Western Inner Mongolia power grid

This paper defines the concept of flexibility in the power system as the ability of the individual components or the system itself to meet the net load changes and respond to power regulation demands. It then reviews the existing methods for evaluating flexibility and their classification, identifies the advantages and disadvantages of these methods, and provides a mathematical model for the flexible operation of the Western Inner Mongolia power grid. Based on the data provided by production units, the paper summarizes the output characteristics of various flexibility resources in the Western Inner Mongolia power grid, including the output curves of typical photovoltaic power stations, typical wind power plants, the overall load curve, typical regional load curves, and power transfer curves of three interconnecting busbars. It also studies the potential regulation capabilities of new energy sources, loads, and interconnecting lines. Finally, the paper evaluates the flexibility level of the Western Inner Mongolia power grid by drawing the net load curve, points out the shortcomings in flexibility in the construction of the new power system in the Western Inner Mongolia region, and provides corresponding suggestions.

Magnetorheological Damper with Variable Displacement Permanent Magnet for Assisting the Transfer of Load in Lower Limb Exoskeleton.

Magnetorheological (MR) fluid exhibits the ability to modulate its shear state through variations in magnetic field intensity, and is widely used for applications requiring damping. Traditional MR dampers use the current in the coil to adjust the magnetic field strength, but the accumulated heat can cause the magnetic field strength to decay if it works for a long time. In order to deal with this shortcoming, a novel MR damper is proposed in this paper, which is based on a variable displacement permanent magnet to adjust the output resistance torque and applied to an exoskeleton joint for human load transfer assistance. A finite element model is used to determine the size parameters of the magnet and separator, so that the maximum output torque is optimal and the torque is uniformly distributed with the magnet displacement. The MR damper was characterized and calibrated on the experimental bench to make it controllable. The novel design enables the torque mass density of the MR damper to reach 8.83Nmm/g, the torque volume density to reach 48.7N/mm2, and has stability for long-term operation. Based on the torque control method proposed, a preliminary human experiment is conducted. The ground reaction force (GRF) data of the subjects is analyzed here, which represents the effect of load transfer to the exoskeleton. Compared with no exoskeleton, the GRF with exoskeleton is significantly reduced: the peak GRF in early stance phase is reduced by 24.14%, and in late stance phase is reduced by 19.77%. Based on our net load benefit (NLB) and net force benefit (NFB) evaluation indicators, the effectiveness of the proposed MR damper exoskeleton for human weight bearing assistance is established.

A Deep Learning Framework for Net Load Forecasting With Unsupervised Behind-the-Meter Disaggregated Data

A Deep Learning Framework for Net Load Forecasting With Unsupervised Behind-the-Meter Disaggregated Data

Data driven net load uncertainty quantification for cloud energy storage management in residential microgrid

Data driven net load uncertainty quantification for cloud energy storage management in residential microgrid

Large-scale offshore wind energy integration by wind-thermal bundled power system: A case study of Yangxi, China

A large-scale offshore wind power integration has a reverse peak regulation effect on the electric load and requires newly built transmission corridors. This work adopts a wind-thermal (WT) bundled system for offshore wind power integration and transmission line capacity factor improvement. The Qingzhou I-VII offshore wind farms (5000 MW) and the Yangxi coal-fired power plant units #5, #6 (2 × 1240 MW) are taken as a case study. The offshore wind farms (OWF) act as the must-take generator to increase the renewable fraction while the thermal power plants (TPP) provide peak regulation service to trace the changing net load. The OWF and TPP share the transmission line and point of interconnection. The OWF integration level is determined by the 8760-h production simulation and the wind-thermal (W-T) capacity ratio is 1.4516:1 (3600 MW OWF) in the studied site. The curtailment rate, electricity shortage rate, transmission line capacity factor (CF), load following precision, renewable fraction, CO2 emission of WT-W3600 condition are 9.02%, 5.41%, 61.36%, 91.43%, 50.46%, 6.97 million ton/year. There is a trade-off relationship between renewable fraction and curtailment rate in the WT system which restricts higher OWF integration. The demand-side response technologies such as power-to-X and energy storage are required to break the trade-off relation and reduce the electricity shortage rate. The WT system is economically viable and the net present value (NPV), levelized cost of electricity (LCOE), internal rate of return (IRR) of WT-W3600 are 3.55 billion¥, 0.4859¥/kWh, 4.82%. Co-locating the OWF and the floating solar photovoltaic (PV) is the future direction that benefits the intensive exploitation of sea area and offshore renewable energy development.

Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment

Unit Commitment (UC) is important for power system operations. With increasing challenges, e.g., growing intermittent renewables and intra-hour net load variability, traditional mathematical optimization could be time-consuming. Machine learning (ML) is a promising alternative. However, directly learning good solutions is difficult in view of the combinatorial nature of UC. This paper synergistically integrates ML within our recent decomposition and coordination method of Surrogate Lagrangian Relaxation to learn “good enough” subproblem solutions of deterministic UC. Compared to original UC, a subproblem is much easier to learn. Nevertheless, predicting good-enough subproblem solutions is still challenging because of the “jumps” of binary decisions and many types of constraints. To overcome these issues, subproblem dimensionality is reduced via aggregating multipliers. Multiplier distributions are novelly specified based on “jumps” for effective learning. Loss functions are innovatively designed to improve prediction qualities. Ordinal Optimization and branch-and-cut are used as backups for unfamiliar cases. Furthermore, online self-learning is seamlessly integrated with offline learning to exploit solutions from daily operations. Results on the IEEE 118-bus system and the Polish 2383-bus system demonstrate that continual learning keeps on improving the subproblem-solving process with near-optimality of the overall solutions maintained. Our method opens a new direction to solve complicated UC.

A distributed peer‐to‐peer energy trading model in integrated electric–thermal system

AbstractTo promote the energy accommodation of both electrical and heating power while considering the source–load uncertainties, a peer‐to‐peer (P2P) energy trading model among prosumers in the integrated electric–thermal system is proposed here, considering the energy trading agent (ETA) with self‐build energy system. The solar heat‐pump hybrid thermal water system (SPTS) and the transferring loss of heating power is considered and modelled based on the principle of steady‐state thermal transfer. Since the variations of load and PV cannot be described by any single common distribution, the chance‐constraints programming based on the Gaussian mixture model (GMM) is proposed to handle the uncertainty of the net load. As the proposed power trading problem is a non‐convex problem with binary variables, an improved alternating direction method of multiplier (ADMM) based on the predictor‐corrector and two‐stage cycle iterations is proposed to enhance the convergence performance of standard ADMM. Finally, an example simulation verifies the effectiveness; this results show that the proposed model can decrease the total cost by 26.7% and enhance local energy balance by 61.8% compared to other cases.

Optimization of an Operation Strategy for Variable Speed Pumped Storage Power System Flexibility

With the increase in the grid-connected scale of new energy, the ability to flexibility regulate a power system is greatly challenged. Since a variable speed pumped storage (VSPS) unit has a wider power regulation range and higher operation efficiency than conventional pumped storage (CPS), this study focuses on improving system flexibility with the VSPS unit. First, the probabilistic characteristics of forced outages affecting the flexibility of power units are studied. Considering the uncertainty of the net load prediction error, a probabilistic density method is proposed to quantify the flexibility of the power system. Second, in order to improve the flexibility of the system and take into account the comprehensive benefits of the VSPS unit, an optimization model for the VSPS unit to improve the flexibility of the grid was established based on bacterial colony chemotaxis that takes into consideration the operational constraints of the VSPS unit. Finally, to verify the effectiveness of the proposed optimization strategy using simulation, the proposed optimization model is used to compare the advantages of the VSPS unit’s ability to regulate system flexibility with a conventional pumped storage unit.

Energy Sharing of Multiple Virtual Power Plants Based on a Peer Aggregation Model

With the increasing number of virtual power plants (VPP) participating in market transactions, the joint operation and energy sharing mode of multiple virtual power plants (multi-VPP) has attracted attention. A peer aggregation model for the multi-VPP energy sharing is proposed based on sharing price. At the VPP autonomous optimization level, each VPP operator formulates an autonomous optimization strategy based on the price incentives and the internal resource parameters and adopts a robust optimization method to improve the strategy’s robustness. At the overall level, a sharing level index is introduced to formulate the sharing price mechanism and an overall sharing strategy is proposed. The case simulation results show that compared with the independent operation of each VPP, participating in energy sharing can effectively promote the overall consumption of renewable energy and the overall operating cost is reduced by 18%. The introduction of the sharing level index into the sharing price can effectively improve the rationality of the formulated sharing price, and the net electricity load fluctuation has a greater impact on the system cost than the thermal load fluctuation.

Multi-Objective Capacity Optimization of Grid-Connected Wind–Pumped Hydro Storage Hybrid Systems Considering Variable-Speed Operation

The coordination of pumped storage and renewable energy is regarded as a promising avenue for renewable energy accommodation. Considering wind power output uncertainties, a collaborative capacity optimization method for wind–pumped hydro storage hybrid systems is proposed in this work. Firstly, considering the fluctuation of wind power generation caused by the natural seasonal weather and inherent uncertainties of wind power outputs, a combined method based on the generative adversarial network and K-means clustering algorithm is presented to construct wind power output scenarios. Then, a multi-objective wind–pumped storage system capacity optimization model is established with three objectives consisting of minimizing the levelized cost of energy, minimizing the net load peak–valley difference of regional power grids, and minimizing the power output deviation of hybrid systems. An inner and outer nested algorithm is proposed to obtain the Pareto frontiers based on the strength of the Pareto evolutionary algorithm II. Finally, the complementarity of wind power and pumped storage is illustrated through an analysis of numerical examples, and the advantages of variable-speed pumped storage in complementary operation with wind power over fixed-speed units are verified.

Multi-scale flexibility assessment of electrical, thermal and gas multi-energy systems based on morphological decomposition

The rapid development of renewable energy has also had an impact on the flexibility of multi energy systems such as electricity, heat, and gas. To analyze the flexible characteristics of multi energy systems at multiple time scales, a multi-scale flexibility evaluation method based on morphological decomposition is proposed. The net load curve is decomposed using mathematical morphology methods, and a multi-scale energy storage configuration method based on the flexibility of electric heating systems is proposed. The analysis data shows that the probability of insufficient upward flexibility, margin expectation, and insufficient expectation of the scale weighted flexibility index are 1.12%, 3.98%, and 1.16%, respectively, while the probability of insufficient downward flexibility, margin expectation, and insufficient expectation are 0.73%, 4.54%, and 0.56%, respectively. The introduction of energy storage and controllable load simultaneously results in an overall downward flexibility index of 0.92% for the system. The results indicate that controllable load can improve the economy of system peak shaving, providing more options for energy storage and configuration in multi energy systems.

Direct short-term net load forecasting in renewable integrated microgrids using machine learning: A comparative assessment

Modern microgrids require accurate net load forecasting (NLF) for optimal operation and management at high shares of renewable energy sources. Machine learning (ML) principles can be used to develop precise and reliable NLF models. This paper evaluates the performance of different ML models, that are optimally trained using supervised learning regimes, for direct short-term net load forecasting (STNLF) in renewable microgrids. Different categories of ML models, such as neural network, ensemble, linear regression, nearest neighbor, and support vector machine were used. The comparative assessment was conducted utilizing historical net load, meteorological, and time-related categorical data acquired from the renewable integrated microgrid of the University of Cyprus in Nicosia, Cyprus. The results showed that all STNLF ML models achieved normalized root mean square error (nRMSE) values below 10%. Amongst the investigated models, the Bayesian neural network (BNN) presented the highest forecasting accuracy, exhibiting a daily average error of 3.58%. In addition, the BNN model yielded robust forecasts regardless of the season and weather conditions. Finally, the results demonstrated that optimally constructed ML models can be applied to provide STNLF in renewable integrated microgrids, which can be used by microgrid operators to efficiently control and manage their assets.

Energy management in citizen energy communities: A flexibility-constrained robust optimization approach considering prosumers comfort

Citizen Energy Communities (CECs) have emerged as a promising solution with numerous benefits for society. CECs have contributed to a higher adoption rate of Distributed Energy Resources (DERs). However, the variable power generation of DERs is a critical challenge, resulting in an increase in the ramp rate of the system's net load. To address this challenge, this paper introduces a unique robust optimization model that utilizes the flexibility constraint to limit the ramp rate of power trading CECs with the upstream grid and assesses the impact of flexibility limits on energy costs. Moreover, two primary goals of CECs are to ensure the prosumer's comfort, which includes thermal comfort, and to achieve self-sufficiency in their energy supply. In this context, this paper uses the Predicted Mean Vote (PMV) index to calculate the thermal comfort of prosumers. Additionally, a sensitivity analysis has been conducted on CECs self-sufficiency. Moreover, a robust optimization approach is utilized to consider the uncertainty in upstream energy prices. The results indicate that the Community Energy Storage (CES) leads to a 34.6% reduction in operation cost. Additionally, the inclusion of CES in CECs allows the Energy Community Manager (ECM) to reduce curtailment power to prosumers by 602.98 kW during their engagement in the self-sufficiency program.

Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads

Building photovoltaic (PV) power generation is intermittent, volatile, random, and uncontrollable; thus, the use of solar grid-connected power generation can lead to a series of problems, such as grid voltage fluctuations and power imbalance. As a typical flexible load, the scheduling and regulation of the heating, ventilation, and air conditioning (HVAC) system load can help a grid-connected solar grid achieve balanced and flexible operation. This study proposes a PV capacity dynamic tracking model predictive control strategy for air-conditioning systems with flexible loads. This strategy aims to effectively address the issue of unstable grid-connected output of solar PV systems. In particular, a solar radiation prediction model based on a long short-term memory neural network optimized by the grey wolf optimization algorithm was established, which effectively improved the model prediction accuracy. The cost function of HVAC flexible load control considers the law of solar PV output and the human thermal comfort model. A genetic algorithm (GA) is used to obtain the optimal control parameters for dynamic regulation. The GA was employed to efficiently and effectively optimize the control of flexible HVAC loads, considering the variability of the solar PV output and ensuring human thermal comfort. A dynamic regulation model was developed for a typical office building with a grid-connected solar PV power generation system. TRNSYS was utilized for verification based on the actual measured data and relevant meteorological parameters of the case building. A new evaluation metric, volatility, was proposed to evaluate net load fluctuation. The simulation platform based on the physical building yield results indicated a strong resemblance between the energy consumption curve under the predicted control conditions using the flexible model and the PV generation curve. Additionally, the net load volatility was measured to be 2.63. Compared with the predicted control condition without the flexible model, the net load volatility of the grid was reduced by 47.08%, and the energy-saving rate reached 10.89% in the summer.

Beyond fixed-speed pumped storage: A comprehensive evaluation of different flexible pumped storage technologies in energy systems

Traditional fixed-speed pumped storage (PS) has been a reliable measure to provide power system flexibility. However, the increasing need for flexibility of power systems due to adverse environmental events and amplified utilization of renewable sources necessitates the need for modernization of PS units. This paper studies the effect of five PS technologies with different operational flexibility in Qinghai Province's energy system. Firstly, PS models considering the difference in minimum stable generation & pumping, unit efficiency, configurations of turbine & pump are established. Then, the energy mixes, planning capacity, inflow, reservoir operation curve and other data are input into the energy system model. Finally, we quantify the effects of different PS forms on the energy system based on technical, economic, and emission indicators, also identify the optimal PS considering cost preference and emission preference. Results show that the investment costs for various pumped storage technologies depend on the motor type and pump capacity. Fixed-speed pump turbine technologies have a lower cumulative power variation of up to 11,225 GWh and the largest number of start-ups in pumping conditions, at 10,857 times. Increasing operational flexibility stabilizes the reservoir water level, and enhancing pump capacity reduces net load fluctuations for ternary PS and variable-speed quaternary PS. Improving wind-solar curtailment is limited by pump capacity, generating flexibility, and reservoir water level. Systems equipped with fixed-speed PS technologies emit more than carbon emissions other PS technologies. Our research outcomes have the potential to provide valuable insights, enabling stakeholders to optimize the deployment of pumped storage flexibility in low-carbon energy systems, taking into account the energy mix and hydrological conditions that are specific to each region.

Price-Based Demand Response: A Three-Stage Monthly Time-of-Use Tariff Optimization Model

In this research, we developed a three-stage monthly time-of-use (TOU) tariff optimization model to address the concerns of confusing time period division, illogical price setting, and incomplete seasonal element consideration in the previous TOU tariff design. The empirical investigation was conducted based on load, power generation, and electricity pricing data from a typical northwest region in China in 2022. The findings indicate the following: (1) In producing the typical net load curves, the employed K-means++ technique outperformed the standard models in terms of the clustering effect by 4.27–26.70%. (2) Following optimization, there was a decrease of 1900 MW in the maximum monthly abandonment of renewable energy, a decrease of 0.31–53.94% in the peak–valley difference, and a decrease of 2.03–17.27% in the monthly net load cost. (3) By taking extra critical peak and deep valley time periods into account, the average net load cost decreased by 10.36% compared with conventional peak–flat–valley time period division criteria.

Optimizing admissible region of nodal net load for active distribution network based on cost/benefit analysis

As the proportion of renewable energy sources (RESs) increases, active distribution network (ADN) operation becomes a more challenging problem due to unpredictable nature of RESs. To address this problem, a new dynamic economic dispatch (DED) for ADN is proposed which can determine the optimal scheduling results based on cost/benefit analysis. Intervals with variable endpoints are applied to describe the nodal net load, which is also named as admissible region in this paper. The admissible region of nodal net load and other scheduling results are simultaneously determined by striking a balance between operation cost when net load varies within the corresponding intervals and benefit represented by reduction of operation risk when net load varies out of their intervals. To facilitate the solution, a new injection shift factor (ISF) of the linearized DistFlow branch equations is proposed, and the voltage magnitude and reactive power can be well addressed, which especially fits for ADN. A surrogate affine policy is applied to avoid the introduction of bilinear terms. And a new step-superimposing linearization method for risk cost is proposed by exploring the specific characteristics of risk cost expression, then binary variables can be eliminated in the model. Finally, this model is recast as a linear programming problem and the computation efficiency can be guaranteed. The validity and effectiveness of the proposed model are illustrated on the IEEE 33-node and 69-node distribution systems.

Real-time capacity cost obligations design in high-renewables energy markets

Under high renewable penetration, the declining energy prices cannot reflect capacity costs of generators. To deal with the missing money problem, several markets around the world have introduced capacity remuneration mechanisms. These mechanisms act well for the generation side by compensating investment costs, but the cost allocation methods among the demand side are non-differentiated or lagging, thus failing to effectively guide consumption patterns. This research proposes a novel two-step cost allocation framework to formulate real-time cost obligations. In replacement of load levels, net load levels (load minus renewable output) are used to measure adequacy conditions under high renewable penetration. The capacity costs are pre-allocated by Shapley value to obtain a mapping function from net load levels to cost obligations. In the real-time, the mapping function is incorporated into the spot market clearing model to generate obligations endogenously. Numerical analyses are conducted on the IEEE test system and an empirical case with California real data. Compared with the lagging obligations formulated by the current mechanism, the proposed framework can reduce peak net load by incentivizing demand responses and storage participation. The profile of real-time capacity cost obligations is illustrated for the California market to show the practicability of the method on a real-market.