Demand-side Uncertainty Research Articles

Planning of renewable energy regional heating system based on demand side uncertainty

Effective planning of renewable energy district heating systems is essential to facilitate the energy transition and achieve environmental goals. In the face of demand-side uncertainty, the complexity of system planning increases significantly. To address this challenge, a new approach has been adopted to optimize heating systems by precisely analyzing and managing demand-side variables. Using simulation experiments to quantitatively assess uncertainty factors, it is found that room temperature, unit ventilation, equipment power, and lighting power had significant effects on system energy consumption, ranging from −17% to 12%. According to these factors, a series of optimization measures are formulated, and then a set of optimization scheme based on demand-side uncertainty is implemented. The experimental results show that the scheme can effectively reduce the daily power output, reduce the total cost input by 7.1%, and achieve a total emission reduction of 4.0% in environmental protection. The optimization strategy proposed in this study significantly reduces energy consumption and costs, and reduces carbon emissions, providing a new solution for the environmental protection and intelligent heating system.

A Wasserstein Distributionally Robust Planning Model for Renewable Sources and Energy Storage Systems Under Multiple Uncertainties

Nowadays, electricity markets and carbon trading mechanisms can promote investment in renewable sources but also generate new uncertainties in decision-making. In this paper, a two-stage Wasserstein distributionally robust optimization (WDRO) model is presented to determine the optimal planning strategy for renewable energy generators (REGs) and energy storage systems (ESSs) in the distribution network. This model considers supply-side and demand-side uncertainties in the distribution network and the interaction uncertainty from the main grid which are depicted by the ambiguity sets based on the Wasserstein metric and historical data. Meanwhile, both 1-norm and -norm Wasserstein metric constraints are considered to satisfy the decision-makers different preference. Furthermore, to solve this WDRO model, a systematic solution method with a three-step process is developed. Numerical results from a modified IEEE 33-node system and a 130-node system in the real world demonstrate the advantages of the two-stage WDRO model and the effectiveness of the solution method.

Dimensioning Microgrids for Productive Use of Energy in the Global South—Considering Demand Side Flexibility to Reduce the Cost of Energy

Microgrids using renewable energy sources play an important role in providing universal electricity access in rural areas in the Global South. Current methods of system dimensioning rely on stochastic load profile modeling, which has limitations in microgrids with industrial consumers due to high demand side uncertainties. In this paper, we propose an alternative approach considering demand side management during system design which we implemented using a genetic scheduling algorithm. The developed method is applied to a test case system on Idjwi Island, Democratic Republic of the Congo (DRC), which is to be powered by a micro hydropower plant (MHP) in combination with a photovoltaic (PV) system and a battery energy storage system (BESS). The results show that the increased flexibility of industrial consumers can significantly reduce the cost of electricity. Most importantly, the presented method quantifies the trade-off between electricity cost and consumer flexibility. This gives local stakeholders the ability to make an informed compromise and design an off-grid system that covers their electricity needs in the most cost-efficient way.

Multi-energy dispatching for uncertainty EV demand: A simulation approach

Uncertainty and randomness in demand and supply bring significant challenges to the stable operation of the grid and the scheduling planning of multi-energy sources. To solve these challenges, we propose and analyze a multi-energy dispatching model which minimizes the total cost and enhances the efficiency of supplying power. Specifically, we design matching algorithms that simulate an appropriately scaled sequence of stochastic EV demand. We also analyze four different energy dispatching scenarios proving that the scheduling model and the multi-energy synergistic microgrid structure can bring higher efficiency and lower costs. Our main contribution is using a simulation approach to take EVs into account for demand-side uncertainty, which significantly improves the efficiency of grid dispatch.

Social equity-based distribution networks design for the COVID-19 vaccine

This study aims to investigate the role of social equity in vaccine distribution network design problems. Inspired by the current COVID-19 vaccine allocation in-country context, we capture social equity-based distribution by modeling three theories: Rawls’ theory, Sadr’s theory, and utilitarianism. We consider various social groups based on degree of urbanization, including inhabitants of cities, towns and suburbs, and rural areas. The distribution problem is subject to, on the one hand, demand-side uncertainty characterized by the daily contamination rate and its space–time propagation that anticipate the in-need population. On the other hand, supply-side uncertainty characterized by the stochastic arrival of vaccine doses for the supply period. To tackle this problem, we propose a novel bi-objective two-stage stochastic programming model using the sample average approximation (SAA) method. We also develop a lexicographic goal programming approach where the social equity objective is prioritized, thereafter reaching an efficiency level. Using publicly available data on COVID-19 in-country propagation and the case of two major provinces in Iran as example of middle-income country, we provide evidence of the benefits of considering social equity in a model-based decision-making approach. The findings suggest that the design solution produced by each social equity theory matches its essence in social science, differing considerably from the cost-based design solution. According to the general results, we can infer that each social equity theory has its own merits. Implementing Rawls’ theory brings about a greater coverage percentage in rural areas, while utilitarianism results in a higher allocation of vaccine doses to social groups compared to the Sadr and Rawls theories. Finally, Sadr’s theory outperforms Rawls’ in terms of both the allocation and cost perspective. These insights would help decision-makers leverage the right equity approach in the COVID-19 vaccine context, and be better prepared for any pandemic crisis that the future may unfold.

An Asymptotically Tight Learning Algorithm for Mobile-Promotion Platforms

Operating under both supply-side and demand-side uncertainties, a mobile-promotion platform conducts advertising campaigns for individual advertisers. Campaigns arrive dynamically over time, which is divided into seasons; each campaign requires the platform to deliver a target number of mobile impressions from a desired set of locations over a desired time interval. The platform fulfills these campaigns by procuring impressions from publishers, who supply advertising space on apps via real-time bidding on ad exchanges. Each location is characterized by its win curve, that is, the relationship between the bid price and the probability of winning an impression at that bid. The win curves at the various locations of interest are initially unknown to the platform, and it learns them on the fly based on the bids it places to win impressions and the realized outcomes. Each acquired impression is allocated to one of the ongoing campaigns. The platform’s objective is to minimize its total cost (the amount spent in procuring impressions and the penalty incurred due to unmet targets of the campaigns) over the time horizon of interest. Our main result is a bidding and allocation policy for this problem. We show that our policy is the best possible (asymptotically tight) for the problem using the notion of regret under a policy, namely the difference between the expected total cost under that policy and the optimal cost for the clairvoyant problem (i.e., one in which the platform has full information about the win curves at all the locations in advance): The lower bound on the regret under any policy is of the order of the square root of the number of seasons, and the regret under our policy matches this lower bound. We demonstrate the performance of our policy through numerical experiments on a test bed of instances whose input parameters are based on our observations at a real-world mobile-promotion platform. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4441 .

Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side.

As an efficient way to integrate multiple distributed energy resources (DERs) and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads (TCLs), energy storage systems (ESSs), price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process (MDP) process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor–critic (Memory A3C, M-A3C) with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training. The multithreaded working feature of M-A3C can efficiently learn the resource priority allocation on the demand side of the microgrid and improve the flexible scheduling of the demand side of the microgrid, which greatly reduces the input cost. Comparison of the researched cost optimization results with the results obtained with the proximal policy optimization (PPO) algorithm reveals that the proposed algorithm has better performance in terms of convergence and optimization economics.

Reliability Evaluation of Cyber–Physical Power Systems Considering Supply- and Demand-Side Uncertainties

To reach effective monitoring and control, a physical power grid couples with a communication network and evolves into cyber–physical power systems (CPPS), but this cyber–physical interdependence may exacerbate failure on the physical/cyber side and may turn into a cascading failure. Furthermore, distributed generators (DGs) and plug-in hybrid electric vehicles (PHEVs) introduced into CPPS add uncertainties to both the supply side and demand side of power energy. In this paper, we detail the model of CPPS and its coupling mechanism in operation and discuss the propagation mechanism of cascading failure within and across a physical power grid and a communication network. For uncertainties of power energy in the supply and demand sides, the generation and load of each day are divided into 24 time segments for modeling. In the case study, the well-being criteria and reliability indexes are employed to analyze the effect of DGs and cyber–physical interdependence on the reliability of CPPS when DGs suffer aging failure and cyber attacks, and the simulations indicate that introducing DGs can effectively enhance the period of healthy and marginal states. Furthermore, cyber attacks can sharply destroy the CPPS compared with aging failure.

Optimal time-differentiated pricing for a competitive mixed traditional and crowdsourced event parking market

An event-based parking pricing problem, the Crowdsourced Event Parking Market Pricing Problem, is proposed wherein parking lot owners and others who are willing to rent out privately owned spaces compete to attract drivers who are looking for available parking spaces. Each parking location owner’s problem is modeled as a bi-level program, where the upper-level parking garages, individuals and consolidators (the players) compete for customers, setting their prices to maximize revenue given the response of the lower-level followers. The lower-level followers choose their parking locations based on the utilities they derive from the spaces, which is a function of the proximity of the spaces to their destinations, parking fees and crowdedness. Prices are set as a function of the time of reservation. Reservation-time based pricing enables price differentiation for late comers either in the form of lower prices to attract last-minute customers when excess spaces are anticipated or higher-pricing if few spaces are expected to remain empty. A multi-period, stochastic Equilibrium Problem with Equilibrium Constraints (EPEC) formulation of the Crowdsourced Event Parking Market Pricing Problem is presented, and a diagonalization method with embedded gradient ascent approach for solution of individual player Mathematical Programs with Equilibrium Constraints (MPECs) is proposed for its solution. Both supply- and demand-side uncertainties are explicitly modeled. Solutions provide competitive parking prices set by reservation time for each parking facility, whether the facility involves a large parking garage or single parking space owner. Numerical experiments were conducted to illustrate the proposed concepts and assess the potential impact of crowdsourced parking spaces on the parking market. The results show that social welfare increases by more than 5% when crowdsourced parking locations account for 7% of the parking market. Results from additional numerical experiments show that ignoring stochasticity results in revenue loss for all parking owners. The developed techniques aim to facilitate existing and new parking facility owners to participate in crowdsourced event-parking markets.

Demand response to improve the shared electric vehicle planning: Managerial insights, sustainable benefits

Massive adoption of shared electric mobility benefits people’s daily commute and environment but creates overload issues into the power grid, then further cause challenges to charging service operations and power management. Previous research always focuses on single optimization process on shared vehicle planning, rather than the combination of demand management into day-ahead planning operations. To this end, we attempt to propose a mixed integer programming model integrating demand response operations to further explore the impacts of demand response on shared electric vehicle planning operations. We first model a two-stages model integrating charging facility location in the first stage and vehicle relocation in the second stage. Moreover, both supply-side and demand-side uncertainties are considered and approximated into tractable form by applying sample average approximation and distributional robust set featuring the entropy knowledge and electric vehicle’s multi-level charging duration. The demand response policy is also proposed to reshape the original charging demand into an economical and reliable way to improve operational efficiency and mitigate the power overload issues caused by massive electric vehicle adoption. Further, we conduct a real-world case study in Amsterdam, the Netherlands, to explore the social-operational impacts of vehicle planning optimization model integrating the demand response, robust charging facility planning in three areas: (1) The demand response integration promote electric vehicle planning operations on cost-saving for about 3%. (2) Data richness of serviceability towards charging piles influence all decisions through the shared electric vehicle charging station planning. (3) A trade-off exists between technical progress on charging rate and charging technology stability.

Multi-Objective Optimization of Steam Power System Under Demand Uncertainty

There are many uncertain factors, which are critical for the efficient operation of steam power systems (SPSs). Due to the modelling inaccuracy and the changing environment, it is challenging to directly formulate the model for SPS operation. In this paper, a method is proposed to alleviate the influence of small-scale fluctuation by discretizing the uncertainty working condition on the SPS. Moreover, the exergy analysis method is proposed to evaluate the quality of different energy and develop a mixed-integer nonlinear model of SPS considering the economic and exergy objectives. Finally, the $\varepsilon $ -constraint method is used to obtain the Pareto front of the multi-objective optimization problem, and the fuzzy decision is used to determine the optimal strategy of the system. By introducing the multi-objective optimization of the SPS model with demand side uncertainty, a flexible scheduling scheme with good economic and exergy objectives is obtained. Comparing with the optimal scheduling without the demand-side uncertainty, numerical results indicate the feasibility and effectiveness of the model.

Optimal Planning Design of a District-Level Integrated Energy System Considering the Impacts of Multi-Dimensional Uncertainties: A Multi-Objective Interval Optimization Method

Improving the utilization efficiency of renewable energy sources (RES) is an important task for the development of an integrated energy system (IES). To address this challenge, this paper proposes a novel multi-objective interval optimization framework for the energy hub (EH) planning problem from the perspective of the source load synergy, while considering the impacts of both supply- and demand-side uncertainties. For this aim, based on an in-depth analysis of the adjustable characteristics of various loads in EH and their effect on RES absorption, an interval model is first established to describe the responsiveness of users’ load demand to real-time energy price variations and its associated uncertainties. In view of the natural contradiction between the system’s economic and environmental benefits, a multi-objective interval optimization model for the EH planning problem is developed, wherein the minimization of the system’s economic costs and the maximization of the RES utilization rate are considered as the dual objectives to be optimized simultaneously. Moreover, this study takes into account the uncertainties of RES availability and demand-side behaviors by using interval numbers and properly considering their impacts in the context of long-term planning. According to the features of the proposed model, the interval order relation and possible degree method are jointly used to transform the model into a deterministic optimization problem first, and then an improved non-dominant sorting genetic algorithm is used to derive the optimal solution to the problem. The results show that the proposed method can effectively improve the economy of EH and the utilization efficiency of RES and flexibly meet different planning requirements, giving better engineering practicability.

Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach

Considering economic and environmental concerns as well as thermal and visual comfort requirements, this paper introduces the idea of Smart Energy-Aware Manufacturing Plant Scheduling for general manufacturing processes, especially high-value-added ones. The integrated system benefits from the use of a grid-connected Microgrid, a combined cooling, heat, and power system, and renewable power. Employing the Conditional Value-at-Risk and guaranteeing both solution and model robustness through an original multi-objective risk-based robust mixed-integer linear programming to optimally support the system make the contribution of the paper novel and unique. The main objective is to schedule manufacturing (non-shiftable) and non-manufacturing (shiftable and interruptible) loads, and distributed energy resources based on the real-time-pricing policy in an interactive decentralized operation of a capacitated multi-carrier energy production-consumption system. Compared to the conventional system, the results outline 64.63%, 1.62%, 100%, and 65.23% less Carbon dioxide emissions, net production, power exchange, and total costs, on average. The proposed framework is preferable to the framework that is based on the use of combined heat and power systems, at least in terms of a reduction in production costs. Considering both the supply- and demand-side uncertainties, the framework performs good trade-offs between solution robustness and model robustness, and environmental and economic concerns.

Optimal Probabilistic Scheduling of a Proposed EH Configuration Based on Metaheuristic Automatic Data Clustering

A combination of various energy conversion units offers a multi-carrier energy system, namely Energy Hub (EH). Optimal scheduling of EHs in the interconnected systems besides providing flexible management in input units stands as a challenging task. The uncertainties of input variables increase the complexity of this task. In this paper, an efficient EH structure is proposed and the conventional model is extended to tackle this issue in a short time operation interval. Moreover, a Metaheuristic Automatic Data Clustering (MADC) scheme used to solve the demand side uncertainty. This strategy omits the use of any additional variable needed for conventional techniques. The final solution is used to solve non differential, and high-dimension EH Economic Dispatch (EHED) problems. For best achievements an enhanced revised configuration by using a variable cost strategy for gas and electricity market simultaneously is proposed too. By comparing the effects of the EH feeding point on the objective function, a management side medium to reducing the computation burden proposed in the proceeding. The simulation results of the proposed model show an increase in EH benefit by reducing the cost. Considering mentioned issues as well as decreasing data dimensions due to reduced computing and probabilistic MADC scheduling of demands which could define the uncertainty. Besides, bypassing the need for a separate load management system, the results of this study are encouraging and warrant further analysis and research. The study is carried out in GAMS© and is connected by MATLAB© to implement the MADC.

Species and Memory Enhanced Differential Evolution for Optimal Power Flow Under Double-Sided Uncertainties

Considering the uncertainty of power generations, in addition to the uncertainty of loads, is more and more important because of the increasing use of renewable energy sources. Most existing works on dynamic optimal power flow (DOPF) have only focused on either the uncertainty of loads (called demand-side uncertainty) or the uncertainty of power generations (called supply-side uncertainty). As far as we know, only a little work on the dynamic OPF problems considered both uncertainties simultaneously. It might be because the combination of variable uncertainties could lead to a huge problem size for existing methods. In this paper, inspired by the ideas in the field of evolutionary dynamic optimization (EDO), we attempt to deal with uncertain parameters from the perspective of tracking the moving optimum. A species and memory enhanced differential evolutionary algorithm (called SMDE) is specially designed to solve the DOPF with double-sided uncertainties. Specifically, in order to deal with the double-sided uncertainties, a modified memory strategy and an improved multi-population strategy were introduced, where the multi-population strategy includes two versions. The experimental results on the modified IEEE 57-bus and 118-bus systems show that the proposed algorithms perform much better than the comparison algorithms for most cases.

Optimal Electricity Distribution Pricing under Risk and High Photovoltaics Penetration

We model a hierarchical Stackelberg game in a competitive power market under high behind-the-meter photovoltaics penetration and demand-side uncertainty, with emphasis on the feedback loop between distributed generation via photovoltaics and power prices. The Stackelberg leader, who is the government regulator, attempts to define a set of network tariffs that results in maximal overall system net benefits with respect to consumer utility, cost recovery and renewable energy promotion. The Stackelberg followers, who are rational consumers of electricity, choose their individual photovoltaics investments in order to maximize their personal utilities. With the consumers’ demand evolution described by a discretized Ornstein–Uhlenbeck process, we find a closed-form approximation to the consumer’s utility, and the existence of a game equilibrium between all the consumers and the regulator. Numerical results are calibrated to PJM power market data, and illustrate the market participants’ coupled decisions. Our results suggest that consumers tend to rely more on photovoltaics when the market demand is more volatile, with potential risks of the utility death spiral where the high electricity retail price resulting from increased distributed generation incentivizes further photovoltaics investment.

An Asymptotically Tight Learning Algorithm for Mobile-Promotion Platforms

Operating under both supply-side and demand-side uncertainties, a mobile-promotion platform conducts advertising campaigns for individual advertisers. Campaigns arrive dynamically over time, which is divided into seasons; each campaign requires the platform to deliver a target number of mobile impressions from a desired set of locations over a desired time interval. The platform fulfills these campaigns by procuring impressions from publishers, who supply advertising space on apps, via real-time bidding on ad exchanges. Each location is characterized by its win curve, i.e., the relationship between the bid price and the probability of winning an impression at that bid. The win curves at the various locations of interest are initially unknown to the platform, and it learns them on the fly based on the bids it places to win impressions and the realized outcomes. Each acquired impression is allocated to one of the ongoing campaigns. The platform's objective is to minimize its total cost (the amount spent in procuring impressions and the penalty incurred due to unmet targets of the campaigns) over the time horizon of interest. Our main result is a bidding and allocation policy for this problem. We show that our policy is the best possible (asymptotically tight) for the problem using the notion of regret under a policy, namely the difference between the expected total cost under that policy and the optimal cost for the clairvoyant problem (i.e., one in which the platform has full information about the win curves at all the locations in advance): The regret under any policy is Ω(√I), where I is the number of seasons, and that under our policy is O(√I). We demonstrate the performance of our policy through numerical experiments on a test bed of instances whose input parameters are based on our observations at a real-world mobile-promotion platform.

Stochastic Risk-Constrained Scheduling of Renewable-Powered Autonomous Microgrids With Demand Response Actions: Reliability and Economic Implications

The unpredictable and volatile nature of renewable energy sources will increase the burden of a system operator for maintaining the system reliability in different conditions. In this article, a stochastic risk-constrained framework is proposed for short-term optimal scheduling of autonomous microgrids to evaluate the influence of demand response (DR) programs on reliability and economic issues, simultaneously. The objective is to maximize the expected profit of the microgrid operator through optimal scheduling of resources in a more reliable manner considering both supply and demand side uncertainties. In the proposed approach, the microgrid operator's risk aversion is modeled by using the conditional value-at-risk method to control and avoid nondesirable profit distributions due to various system uncertainties. Moreover, ac optimal power flow technique is employed to calculate the amount of energy and reserve of dispatchable distributed generation (DG) units and responsive loads for the operational hour of the next day. Eventually, the applicability of the proposed method is studied on different test systems and impacts of various parameters such as level of DR participants and values of lost load as well as risk aversion parameter on the system's economy and reliability indices are investigated deeply.

Planning Method of Regional Integrated Energy System considering Demand Side Uncertainty

Abstract In this paper, a robust planning method of regional integrated energy system (IES) considering the uncertainty of cooling, heat and electrical loads on the demand side is submitted. First, the energy hub (EH) model of regional IES with Combined Cooling Heating and Power (CCHP), electric air-conditioning unit (EC) and gas boiler (GB) is established. Second, the uncertainty of load is described by the method of adjustable interval, and the robust planning model of regional IES is formed. Third, the robust programming model can be transformed into a convex mixed integer planning model, and then solved. Finally, the case study is carried out with a comprehensive area of Tianjin in China, the results are analyzed to verify the effectiveness of the proposed planning method.

Interactive Energy-Saving Dispatch Considering Generation and Demand Side Uncertainties: A Chinese Study

The energy-saving dispatch is becoming one of the most important smart grid components in China, which asks for both generation and demand side resources' participation. In smart electricity grid, the uncertainties related with variable renewable energy outputs on the generation side, as well as the deviations between the scheduled demand response (DR) resources and their actual availabilities on the demand side should be simultaneously taken care of. In this paper, a new energy-saving dispatch problem is studied considering the uncertainties from both generation and demand sides. A bi-level optimization model is established to address the generation and demand side's interactions considering economic dispatch of thermal units and load control schemes of end users in the new energy-saving dispatch problem. The upper-level minimizes the electric transmission company's power generation and carbon emission costs while maintaining the system reliability, and the lower-level minimizes the expected total load dispatch cost paid by the electric distribution company to DR providers and the demand side emission cost. An iterative algorithm is adopted to solve the proposed bi-level model. In addition, improved NSGA-II method is used to solve the lower-level multi-objective optimization problem for seeking optimal compromise solution. Numerical case studies on the practical energy-saving dispatch problem of Guangdong Province in China illustrate the effectiveness of the proposed approach.