Uncertainty In Electricity Demand Research Articles

Reliability assessment of generation capacity in modern power systems via analytical methodologies

With the recent transition to a low-carbon electrical power system (EPS), the large-scale utilization of renewable energy resources in electrical power generation introduces a substantial amount of uncertainty on the generation side of the EPS. This uncertainty, along with the inherent uncertainty of electricity demand, makes assessing generation reliability a very computationally intensive process. To enhance the computation efficiency of EPS generation reliability assessment, it is crucial to have an efficient probabilistic model of available generation capacities that strikes a balance between improved computational performance and model accuracy. In this paper, various probabilistic models are proposed to characterize the variability and uncertainty of conventional and renewable power generations (photovoltaic and wind). On the basis of these models, an analytical formulation of probabilistic reliability indices (RIs) is implemented. The computation time and accurate RIs values found using the Monte Carlo simulation method serve as the basis for reporting solving time improvements with corresponding losses in the accuracy of the RIs for different analytical methodologies. The results of multiple case studies of an EPS are presented, considering various combinations of conventional and renewable generation capacity, levels of renewable power penetration, and system reliability levels. The results indicate the practical implementation of analytical assessment methodologies compared to the simulation method in terms of accuracy and computational effort. This study is of immediate relevance and potential importance to operational reliability and generation expansion planning studies in EPSs.

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy.

A Multitask Integrated Deep-Learning Probabilistic Prediction for Load Forecasting

Spinning reserve without accurate load forecasting can lead to automatic disconnection of critical loads by under-frequency load shedding devices. Such a predicament poses a grave threat to the economic and social welfare of the affected region, and in extreme scenarios, can result in a debilitating grid collapse. However, existing models lack the deep feature extraction capability and cannot accurately predict the uncertainty of electricity demand. Thus, a multitask integrated deep-learning probabilistic prediction with multidimensional feature extraction based on granularity information and quantile regression is explored in this study to solve the insufficient feature extraction problem in probabilistic prediction. In the proposed scheme, we designed a multi-layer feature extraction framework, which information granularity based on fuzzy rough sets to extract time-domain features, multilayer Laplace operators to extract features globally, and quantile regression recurrent neural network variants to extract time context information. In addition, the Pareto integration method is extended to capture better individuals. The experimental results demonstrate that the proposed system can effectively improve deterministic forecasting and uncertainty analysis of electricity demand and manage daily fluctuations.

REVIEW ON VOLTAGE SAG STUDIES FOR DISTRIBUTION GRID INCLUDING RENEWABLE ENERGY SOURCES

Several types of power quality problems can be observed in power network. These power quality problems include voltage unbalance, voltage flicker, voltage sags and swells, interruptions, and voltage and current harmonics. Voltage sag is one of the vital power quality issues. This problem can cause a decrease in power grid voltage over a short time horizon. The aforementioned problem is caused by various types of short circuits and the widespread use of sensitive devices. These problems have a significant impact on power systems. In this review article, the different ways to prevent voltage sag have been presented by considering literature studies. Concerning voltage sags in radial and mesh grids, the equipment used, and methods have all been taken into account. In these studies, the articles about voltage sag-related problems have been put into groups based on certain criteria. These criteria include the consideration of uncertainties in electricity demand or renewable sources. The recommendations related to the voltage sag studies have been presented. These suggestions are based on the inclusion of electricity consumption and renewable energy source uncertainties. The future works have been mentioned by considering these load and renewable system uncertainties.

A Bilevel Stochastic Optimization Framework for Market-Oriented Transmission Expansion Planning Considering Market Power

Market power, defined as the ability to raise prices above competitive levels profitably, continues to be a prime concern in the restructured electricity markets. Market power must be mitigated to improve market performance and avoid inefficient generation investment, price volatility, and overpayment in power systems. For this reason, involving market power in the transmission expansion planning (TEP) problem is essential for ensuring the efficient operation of the electricity markets. In this regard, a methodological bilevel stochastic framework for the TEP problem that explicitly includes the market power indices in the upper level is proposed, aiming to restrict the potential market power execution. A mixed-integer linear/quadratic programming (MILP/MIQP) reformulation of the stochastic bilevel model is constructed utilizing Karush−Kuhn−Tucker (KKT) conditions. Wind power and electricity demand uncertainty are incorporated using scenario-based two-stage stochastic programming. The model enables the planner to make a trade-off between the market power indices and the investment cost. Using comparable results of the IEEE 118-bus system, we show that the proposed TEP outperforms the existing models in terms of market power indices and facilitates open access to the transmission network for all market participants.

Combining Daily Electricity Forecasts Based on DBSCAN and Stacking Fusion

Background: Modern upgrades of power grids and a rapidly expanding economy complexify the uncertainties of electricity demand. Objective: The objective of the study is to have a more precise prediction on the demand side, which is beneficial in affirming the stable operation of the power system. Methods: This paper presents a combined electricity forecasting method based on the users clustering and stacking ensemble learning to mine underlying properties of different individual consumers. The preprocessed electricity consumption profiles are inputted into the DBSCAN clustering algorithm to obtain the clusters. The alternative models are tailored for different clusters in the stacking fusion framework for training and testing. Result: Experimental results on the operating data of Shandong Power Grid show that the proposed method has higher prediction accuracy and better generalization ability. Conclusion: The framework is of great significance for improving the level of power supply service.

Generic Framework for the Optimal Implementation of Flexibility Mechanisms in Large-Scale Smart Grids

This paper aims to provide the smart grid research community with an open and accessible general mathematical framework to develop and implement optimal flexibility mechanisms in large-scale network applications. The motivation of this paper is twofold. On the one hand, flexibility mechanisms are currently a hot topic of research, which is aimed to mitigate variation and uncertainty of electricity demand and supply in decentralised grids with a high aggregated share of renewables. On the other hand, a large part of such related research is performed by heuristic methods, which are generally inefficient (such methods do not guarantee optimality) and difficult to extrapolate for different use cases. Alternatively, this paper presents an MPC-based (model predictive control) framework explicitly including a generic flexibility mechanism, which is easy to particularise to specific strategies such as demand response, flexible production and energy efficiency services. The proposed framework is benchmarked with other non-optimal control configurations to better show the advantages it provides. The work of this paper is completed by the implementation of a generic use case, which aims to further clarify the use of the framework and, thus, to ease its adoption by other researchers in their specific flexibility mechanism applications.

A Novel Risk-based Operational Model for an Islanded Microgrid with Electric Vehicle Parking Lots, Energy Storage Devices and Flexible Demand

In traditional networks, power transmission from production centers to consumption centers causes many issues such as energy losses, decreased reliability, and low power quality. These issues have given rise to a new trend in electricity networks known as microgrids. This paper presents a new hybrid two-stage operational model based on the information gap decision theory (IGDT)/stochastic method for optimum energy management of an islanded microgrid under uncertainties. The suggested model investigates the uncertainty associated with wind energy using the IGDT method without using a probability distribution function or scenario creation. Uncertainties in electricity demand and vehicle owners behavior are also examined using a two-stage stochastic method. The suggested hybrid method, which is described as a bi-level two-stage optimization framework, benefits from both IGDT and scenario-based stochastic programming methods. Furthermore, the proposed microgrid includes new energy sources such as intelligent electric vehicle parking lots, energy storage devices, and demand response programs, all of which work together to decrease the cost of daily operation. According to numerical findings, the optimum utilization of new energy sources under the suggested hybrid approach lowers operational costs by 4.8%.

Optimizing generation expansion planning with operational uncertainty: A multistage adaptive robust approach

This paper presents a multistage adaptive robust generation expansion planning model, which accounts for short-term unit commitment and ramping constraints, considers multi-period and multi-regional planning, and maintains the integer representation of generation units. The uncertainty of electricity demand and renewable power generation is taken into account through bounded intervals, with parameters that permit control over the level of conservatism of the solution. The multistage robust optimization model allows the sequential representation of uncertainty realization as they are revealed over time. It also guarantees the non-anticipativity of future uncertainty realizations at the time of decision-making, which is the case in practical real-world applications, as opposed to two-stage robust and stochastic models. To render the resulting multistage robust problem tractable, decision rules are employed to cast the uncertainty-based model into an equivalent mixed integer linear (MILP) problem. The re-formulated MILP problem, while tractable, is computationally prohibitive even for moderately sized systems. We, thus, propose a solution method relying on the reduction of the information basis of the decision rules employed in the model, and validate its adequacy to efficiently solve the problem. The importance of considering multistage robust frameworks for accounting for net-load uncertainties in generation expansion planning is illustrated, particularly under a high share of renewable energy penetration. A number of renewable penetration scenarios and uncertainty levels are considered for a case study covering future generation expansion planning in Europe. The results confirm the effectiveness of the proposed approach in coping with multifold operational uncertainties and for deriving adequate generation investment decisions. Moreover, the quality of the solutions obtained and the computational performance of the proposed solution method is shown to be suitable for practical policy-making generation expansion planning problems, seeking to evaluate the impact of uncertainty on future system-wide performance.

Sensitivity analysis of cooling demand applied to a large office building

Previous dynamic building simulation studies have not often focused on analysing the sensitivity of peak loads to input parameters. However, these peak loads may have a critical impact on system design capacities and power network operation. This study aims to examine the sensitivity of cooling demand related results (total electricity demand, HVAC end-use and space cooling) in a large office building using two global sensitivity analysis methods: Morris elementary effect and Sobol indices. More specifically, this paper examines the implications of different type of climates to the uncertainty in these different cooling output results and the sensitivity of the different parameters for each result. Moreover, this paper investigates the difference between the effects of annual and peak analysis for cooling demand of office buildings, which can provide insight on cooling demand from the perspectives of total cooling energy and system capacity for building cooling systems, respectively.This study has found that generally, the changes are more significant for peak demand than for annual demand. The coefficient of variation for the total peak demand is around 25% and 21% for total annual demand. This study identifies that the ventilation rate is the parameter that contributes the largest for the uncertainty in electricity demand of the HVAC end-use, between 50% and 70% of the change (ST), both for annual and peak demand. Regarding the effect on total electricity demand, ventilation rate is still one of the most critical factors, but equipment and lighting densities also become a significant contributor to the sensitivity of the total demand.

A stochastic programming model for an energy planning problem: formulation, solution method and application

The paper investigates national/regional power generation expansion planning for medium/long-term analysis in the presence of electricity demand uncertainty. A two-stage stochastic programming is designed to determine the optimal mix of energy supply sources with the aim to minimise the expected total cost of electricity generation considering the total carbon dioxide emissions produced by the power plants. Compared to models available in the extant literature, the proposed stochastic generation expansion model is constructed based on sets of feasible slots (schedules) of existing and potential power plants. To reduce the total emissions produced, two approaches are applied where the first one is performed by introducing emission costs to penalise the total emissions produced. The second approach transforms the stochastic model into a multi-objective problem using the \(\epsilon \)-constraint method for producing the Pareto optimal solutions. As the proposed stochastic energy problem is challenging to solve, a technique that decomposes the problem into a set of smaller problems is designed to obtain good solutions within an acceptable computational time. The practical use of the proposed model has been assessed through application to the regional power system in Indonesia. The computational experiments show that the proposed methodology runs well and the results of the model may also be used to provide directions/guidance for Indonesian government on which power plants/technologies are most feasible to be built in the future.

Innovative strategy to design a mixed resilient-sustainable electricity supply chain network under uncertainty

Motivated by a real-world case, this paper deals with uncertainty issues in the resilient and sustainable electricity supply chain network design. Uncertainty is always found in electricity demand prediction and perfect foresight is not possible. The resilience of electricity networks in face of uncertainty can prevent catastrophic impacts. Besides, due to the growing concerns about social impacts, considering social measures along with the other measures is becoming critical when analyzing the network costs. In this paper, a multi-objective optimization model is developed, which consists of the total cost in the first objective, resiliency measures in the second objective, and some aspects of corporate social responsibility in the third objective. The resiliency measures minimize de-resiliency, including successive establishment, distributed generators inadequacy, congestion through electrical lines, and energy dissatisfaction level. A novel robust approach is also proposed to deal with the uncertainty of electricity demand based on the light robust programming and possibilistic theory in the fuzzy logic. By investigating a real case in Iran, the contributions of the considered measures, and the effects of uncertainty are analyzed. The analyses reveal that by applying the proposed model the decision-makers can enhance corporate social responsibility and resiliency by 50% and 20%, respectively, although it increases the total cost by 50%. Moreover, the results of applying the proposed fuzzy-robust approach show that how the decision-makers can effectively allocate the uncertainty budget and protection level to attain more robust solutions in terms of standard deviation and mean value of the total cost.

Simultaneous and sequential stochastic optimization approaches for pumped storage plant scheduling with random breakdowns

This paper addresses a pumped-storage stations scheduling problem. The study contribution is three-fold. First, a new service-related multi-objective function is proposed. The model aims to minimize the supply-demand disparity function and the pump maintenance cost in terms of the number of pump switches. Second, the proposed mathematical formulation considers individual units scheduling and random breakdowns. Furthermore, this work considers the electricity demand uncertainty in hourly and daily basis. Third, a new sequential two-stage formulation is proposed. The Sample Average Approximation method is applied to handle the grid demand uncertainty, stochastic failures and unplanned maintenance of pumping units. Goal programming is applied at the second-stage to handle the conflicting objective functions and avoid the model infeasibility. A case study data based on the Ingula pumped-storage station in South Africa is used to test the performance of the proposed approach. A comparison between the two-stage sequential approach and a simultaneous approach showed the superior performance of the two-stage sequential approach in terms of modeling complexity and computational time. Furthermore, experimental analysis showed that involving more pumping/generating units could improve the service level by reducing Disparity Index. Moreover, the two-stage sequential approach showed a superior performance even for the larger size instances.

Resolving conflict objectives between environment impact and energy efficiency – An optimization modeling on multiple-energy deployment

Energy consumption increases dramatically due to economic development, leading to the raising of toxic materials such as CO2 during electricity generation process. This study aims at solving the electricity generation portfolio issue to maximize electricity generation while minimizing the CO2 emission to protect the environment. In this research, multiple types of power plants to produce electricity, such as by gas, coal, solar, are alternatives for electricity generation portfolio. Especially, uncertainty in electricity demand is included and dealt by a stochastic chance constrained method. Further. The proposed model is solved by a non-dominated sorting genetic algorithm for multiple objectives. The result presents the optimal capacity portfolio plan for electricity generation and suggests a trade-off decision between conflicting goals to balance electricity generation efficiency and environmental protection.

Methodology for the Energy Need Assessment to Effectively Design and Deploy Mini-Grids for Rural Electrification

In order to successfully deploy a large number of decentralized energy systems in developing countries, it is necessary to standardize effective methodologies and procedures to develop off-grid/mini-grid systems. Considering that the energy need assessment provides inputs and assumptions used in business modelling and mini-grid design, the accuracy of its results directly affects the technical and financial feasibility studies. Thus, the approach for applying a proven methodology for the energy need assessment of rural communities is aimed at obtaining reliable input data for the mini-grid development. This helps in reducing both the financial challenges by mitigating the uncertainties in electricity demand and the technical challenges by contributing to adequately size off-grid power generation systems, with a view to boost toward a common overall objective of mini-grid’s optimization methods and tools. Hence, taking into consideration that target communities differ in terms of needs and context conditions, the proposed paper describes an inclusive methodology that can be adapted case-by-case. It provides an effective applied solution the lack of proven guidelines from project developers or literature, giving priority to data collection methods able to achieve a large sample representative of the market, with high accuracy in estimating the energy consumptions from electricity substitutes.

Generation capacity expansion under demand, capacity factor and environmental policy uncertainties

Abstract This paper presents an extension and application of the two-stage stochastic optimization methodology to determine electricity generation technology choices and capacity additions under sequential and multiple uncertainties. Specifically, the model presented in this paper addresses how uncertainties in electricity demand, capacity factor of wind and solar technologies, and environmental policy combine to affect the capacity additions of electricity generation technologies. This study unpacks the impact of these uncertainties to find that, on the one hand, when policy uncertainty is isolated from uncertainties in demand and capacity factor, neither of the two uncertainties dominates the other; instead they are complementary. Capacity factor uncertainty will dictate the types of technologies to be deployed while demand uncertainty will determine the amount of capacity to be installed or purchased. On the other hand, a combination of the three dimensions of uncertainty highlights intriguing insights that demonstrate the dominance of one uncertainty over the other. Under two different environmental policies, emissions standards and carbon taxes, the optimal prescription of generation technology combinations with respect to capacity additions are varied. However, we find that carbon tax will be more efficient at reducing CO2.

Power Generation Expansion Optimization Model Considering Multi-Scenario Electricity Demand Constraints: A Case Study of Zhejiang Province, China

Reasonable and effective power planning contributes a lot to energy efficiency improvement, as well as the formulation of future economic and energy policies for a region. Since only a few provinces in China have nuclear power plants so far, nuclear power plants were not considered in many provincial-level power planning models. As an extremely important source of power generation in the future, the role of nuclear power plants can never be overlooked. In this paper, a comprehensive and detailed optimization model of provincial-level power generation expansion considering biomass and nuclear power plants is established from the perspective of electricity demand uncertainty. This model has been successfully applied to the case study of Zhejiang Province. The findings suggest that the nuclear power plants will contribute 9.56% of the total installed capacity, and it will become the second stable electricity source. The lowest total discounted cost is 1033.28 billion RMB and the fuel cost accounts for a large part of the total cost (about 69%). Different key performance indicators (KPI) differentiate electricity demand in scenarios that are used to test the model. Low electricity demand in the development mode of the comprehensive adjustment scenario (COML) produces the optimal power development path, as it provides the lowest discounted cost.

Estimation of extreme inter-day changes to peak electricity demand using Markov chain analysis: A comparative analysis with extreme value theory

Uncertainty in electricity demand is caused by many factors. Large changes are usually attributed to extreme weather conditions and the general random usage of electricity by consumers. More understanding requires a detailed analysis using a stochastic process approach. This paper presents a Markov chain analysis to determine stationary distributions (steady state probabilities) of large daily changes in peak electricity demand. Such large changes pose challenges to system operators in the scheduling and dispatching of electrical energy to consumers. The analysis used on South African daily peak electricity demand data from 2000 to 2011 and on a simple two-state discrete-time Markov chain modelling framework was adopted to estimate steady-state probabilities of two states: positive inter-day changes (increases) and negative inter-day changes (decreases). This was extended to a three-state Markov chain by distinguishing small positive changes and extreme large positive changes. For the negative changes, a decrease state was defined. Empirical results showed that the steady state probability for an increase was 0.4022 for the two-state problem, giving a return period of 2.5 days. For the three state problem, the steady state probability of an extreme increase was 0.0234 with a return period of 43 days, giving approximately nine days in a year that experience extreme inter-day increases in electricity demand. Such an analysis was found to be important for planning, load shifting, load flow analysis and scheduling of electricity, particularly during peak periods.

Developing a CCHP- microgrid operation decision model under uncertainty

Abstract In this study, we present a collaborative decision model to study the energy exchange among building clusters where the buildings share a combined cooling, heating and power system, thermal storage, and battery, and each building aims to minimize its energy consumption cost under electricity demand uncertainty. The problem is formulated as a two-stage stochastic programming model and then solved using a hybrid decomposition algorithm that combines Sample Average Approximation algorithm with an enhanced Benders decomposition algorithm. Numerical experiments reveal that the hybrid decomposition algorithm provides high quality feasible solution in solving the realistic large-scale building cluster problem in a reasonable amount of time. Experimental results allow the investors to decide the optimal sizing of thermal and battery storage and PGU capacities under power demand uncertainty. Further, the model can assist decision makers to choose the appropriate pricing mechanism (i.e., an optimal pricing plan) under different electricity demand variability level. Finally, we observe that the CCHP-microgrid system is more sensitive to an increase in heating demand than the cooling demand.

Optimal supply and demand bidding strategy for an aggregator of small prosumers

This paper addresses the problem faced by an aggregator of small prosumers, when participating in the energy market. The aggregator exploits the flexibility of prosumers’ appliances, in order to reduce its market net costs. Two optimization procedures are proposed. A two-stage stochastic optimization model to support the aggregator in the definition of demand and supply bids. The aim is to minimize the net cost of the aggregator buying and selling energy at day-ahead and real-time market stages. Scenario-based stochastic programing is used to deal with the uncertainty of electricity demand, end-users’ behavior, outdoor temperature and renewable generation. The second optimization is a model predictive control method to set the operation of flexible loads in real-time. A case study of 1000small prosumers from the Iberian market is used to compare four day-ahead bidding strategies and two real-time control strategies, as well as the performance of combined day-ahead and real-time strategies. The numerical results show that the proposed strategies allow the aggregator to reduce the net cost by 14% compared to a benchmark typically used by retailers (inflexible strategy).