Demand Response Uncertainty Research Articles

A distributionally robust optimization approach of multi-park integrated energy systems considering shared energy storage and Uncertainty of Demand Response

To enhance the economic efficiency and renewable energy integration capacity of multi-park integrated energy systems (MPIES) and address the issue of insufficient consideration of demand response uncertainty in existing studies, this paper proposes a distributionally robust optimization approach for multi-park integrated energy systems, considering shared energy storage and the uncertainty of demand response. First, models of the shared energy system and demand response are established. Based on these models, a deterministic optimization scheduling model for MPIES is developed, aiming to minimize system costs while considering constraints such as grid power balance. To address uncertainty in demand response, this paper employs Interval Type-2 Fuzzy Theory to construct uncertainty sets and considers system reliability constraints. The original optimization problem is then transformed into an equivalent robust counterpart model, and the optimal distributionally robust solution is obtained through parameter domain decomposition methods. Finally, the proposed method is validated using the IEEE 33-bus system. The results show that considering shared energy storage and demand response individually can reduce total system costs by 4.86 % and 26.46 %, respectively. After accounting for the uncertainty in demand response, the total system cost increases only slightly by 4.36 %, but this improves the system's robustness.

Research on optimal dispatch model of power grid considering the uncertainty of flexible resource demand response on the residential side

AbstractResidential side flexible load participation in demand response (DR) has been widely used in recent years. However, there are uncertainties in the response willingness and response volume of residential customers, which have a non‐negligible impact on the security and economic dispatch of the power sector. This paper analyzes the uncertainty factors in the demand response process of residential users from the equipment load layer–user layer, establishes for the first time a refined model of residential users' participation in DR from the perspectives of participation uncertainty and response uncertainty, and further constructs a grid optimal dispatch model considering DR uncertainty. The experimental simulation results show that the model established in this paper is able to adjust the operation strategy according to the requirements of user comfort, enhance the enthusiasm of users to participate in DR, and at the same time reduce the dispatch security risk and economic loss caused by the uncertainty of residents' DR on the power grid.

Low-Carbon economic scheduling with Demand-Side response uncertainty in regional integrated energy system

Effective mitigation and reduction of carbon emissions is consistently underscored in Regional Integrated Energy Systems (RIES), aligning with the goals of carbon-neutralization. However, the inherent trade-off between carbon emissions reduction and economic operation is an obvious challenge not only in power systems but especially in the coupled electricity-gas-heat energy systems. This paper develops a bi-level low-carbon optimal scheduling model, highlighting reliable operation and the balance of supply and demand of RIESs with demand-response uncertainty. In this paper we firstly redefined the carbon emission of a RIES as a carbon flow and proposed a reward and punishment carbon trading mechanism based on the ladder carbon price. Secondly, we designed an incentive-based demand-side response policy within the subsystem encompassing electricity, gas, and heat. Concurrently, the probability density distribution functions of transferable, replaceable, and interruptible loads are computed separately. Based on the above, the deviation of the load reduction and the actual load is recalculated to delimit the constraint of the demand-side response. Finally, the mechanism of carbon trading and the demand-response is leveraged to address the economic scheduling incorporating operation cost, carbon transaction cost, imbalance costs of supply and demand, and load loss. Simulations demonstrated in the paper prove that the optimal solution with demand-response uncertain may effectively increase the economic performance and reduce carbon emissions of a RIES.

Dual-Stackelberg game-based trading in community integrated energy system considering uncertain demand response and carbon trading

Given the uncertainties in integrated demand response and carbon trading mechanism, this paper utilizes a dual-Stackelberg game framework to propose trading strategies among multiple entities within such a system. The innovation of this study lies in its development of a collaborative optimization method that encompasses extensive competition between the supply and demand sides, considering the synergistic incentives of carbon trading on the source-side and uncertain responses on the load-side. Firstly, a typical system framework is constructed involving various participants, clarifying energy and carbon market mechanisms and incorporating users’ psychological characteristics in demand response uncertainty. Secondly, a dual-Stackelberg game model is established, and the existence and uniqueness of game equilibrium are demonstrated. Finally, the model's effectiveness is validated through various scenarios. The results indicate that the equilibrium benefits of each entity are 6643.81, 7747.16, 8029.01 yuan, and 9326.62 yuan. The carbon trading mechanism can effectively constrain and reduce the system's carbon emissions by 10.20 %. Furthermore, accounting for user uncertainties in integrated demand response behavior leads to an 18.54 % increase in the peak-to-valley difference in electricity demand. It is concluded that the presented methodology provides innovative insights for addressing trading challenges within hierarchical integrated energy markets.

Research on Coordination Planning Model of Source-Grid-Load-Storage Considering Demand Response Uncertainty

With the integration of wind power, photovoltaic, and other new energy into the grid, the growth of carbon emissions has been effectively suppressed, which greatly contributes to the realization of the “Carbon peak, Carbon neutral’’. However, the randomness and instability of new energy generation also greatly affect the stable operation of the power grid. At present, the combination of demand response and power system can give full play to the maximum potential of new energy. This is an effective method to realize stable and optimal operation of power system. Based on this, this paper first constructs the SOC output characteristic model of energy storage and considers the DLC and time-of-use price as well as different demand response types. The robust optimization method is used to deal with the uncertainty of demand response. Secondly, a multi-objective function and constraint conditions are constructed to minimize the system planning cost, operation cost, and pollution emission. Finally, the genetic algorithm is used to analyze an example in a certain area of Fujian. In the example, the economy of planning schemes under different robust control parameters is compared and analyzed. The results show that the model and calculation method in this paper are beneficial to delaying the investment in power systems, and can provide decision-making reference for mining and utilizing demand response resources, reducing system planning costs and improving operation efficiency.

An optimal bidding and scheduling method for load service entities considering demand response uncertainty

With the rapid development of demand-side management technologies, load serving entities (LSEs) may offer demand response (DR) programs to improve the flexibility of power system operation. Reliable load aggregation is critical for LSEs to improve profits in electricity markets. Due to the uncertainty, the actual aggregated response of loads obtained by conventional aggregation methods can experience significant deviations from the bidding value, making it difficult for LSEs to develop an optimal bidding and scheduling strategy. In this paper, a bi-level scheduling model is proposed to maximize the net revenue of the LSE from optimal DR bidding and energy storage systems ESS scheduling by considering the impacts of the uncertainty of demand response. An online learning method is adopted to improve aggregation reliability. Additionally, the net profit for LSEs can be raised by strategically switching ESS between two modes, namely, energy arbitrage and deviation mitigation. With Karush–Kuhn–Tucker (KKT) optimality condition-based decoupling and piecewise linearization applied, this bi-level optimization model can be reformulated and converted into a mixed-integer linear programming (MILP) problem. The effectiveness and advantages of the proposed method are verified in a modified IEEE RTS-24 bus system.

Risk-averse TSO-DSOs coordinated distributed dispatching considering renewable energy and demand response uncertainties

The renewable energy generation (REG) integration has introduced a significant amount of uncertainties to power systems. This new trend requires power systems to have sufficient flexibility to mitigate the power imbalance between supply and demand. Demand response (DR), as a potential flexibility resource, has gained widespread attention in recent years. However, communication failures or customers’ unexpected behaviors may result in DR uncertainties, leading to operational risks. To cope with the impacts of DR and REG uncertainties, this work proposes a stochastic programming-based risk-averse optimal dispatch model to reduce both electricity purchasing cost and potential risk cost through transmission network (TN) and active distribution networks (ADNs) coordinations. The risk-averse distributed optimal dispatch problem is formulated as two sub optimization models. One conducts the transmission-level risk-averse generation dispatch, while the other represents the distribution-level ADN market clearings. Moreover, the conditional value at risk (CVaR) is used to measure the potential risk cost caused by DR and REG uncertainties. Finally, the optimization model is solved by a distributed solution algorithm, alternating direction method of multipliers (ADMM), to preserve data privacy between multiple stakeholders and reduce communication requirements. The performance of the proposed approach is evaluated in T30-D2 × 33 and T118-D85 + D2 × 69 test systems, the results validate its effectiveness.

Optimal operation for integrated electricity and natural gas systems considering demand response uncertainties

The volatility of wind power and the uncertainty of demand response have brought greater challenges to the operation of integrated electricity and natural gas systems. Therefore, an optimal operation model considering comprehensive demand response uncertainty is proposed. First, chance-constrained is used to deal with the uncertainty of wind power. The uncertainty of three demand response loads that can be transferred, reduced, and replaced are modeled respectively. Secondly, considering that the two systems are different stakeholders, a distributed optimization model is established, the mixed-integer nonlinear model established by incremental linearization and second-order cone relaxation processing is further used, and the distributed optimization model is realized through target cascade (ATC). Finally, a case study of the integrated electricity and natural gas system composed of an IEEE 33-bus power system and 24-node natural gas system verifies that: 1) considering the demand response will reduce the system cost by 3.57%; 2) the necessity of considering the uncertainty of demand response; 3) the convergence of this method is better than ADMM method.

Two-Stage Robust Optimal Scheduling of “Nearly-Zero-Carbon Park” Considering the Synergy of Waste Disposal and Carbon Capture Gas-Fired Power Plants

Aiming at the demand characteristics of “Nearly-zero Carbon Parks" (NZCP) powered by all renewable energy for low-carbon emissions and waste reduction, a NZCP topology and a two-stage robust optimal scheduling model based on the Carbon Capture Gas-fired Power Plant (CCGPP)-Power to Gas(P2G)-Waste Disposal (WD) collaborative model are proposed in this paper. First, the relationship between WD, CCGPP and energy supply and demand that can be generated from renewable energy is studied, and according to the system energy regulation requirements, the energy flexibility of residential loads, small industrial loads and electric vehicles are considered, and the NZCP mathematical model based on the CCGPP-P2G-WD collaborative model is established. Secondly, NZCP energy supply and demand characteristics are studied, and an energy coordination model for NZCP energy conversion and storage powered by 100% renewable energy is established. Then, wind power, photovoltaic power, and combined demand response uncertainties are considered, and the NZCP two-stage robust hybrid optimal dispatch model is established based on second-order cone relaxation and dual theory. Finally, the simulation analysis is carried out based on the operation data of the integrated energy system of a park in a certain area in northern my country. The simulation results show that the proposed two-stage robust optimal scheduling model of NZCP coordinated with WD and CCGPP can effectively improve the economy of the system at a higher energy balance level, improve the absorption capacity of renewable energy, and provide better multi-time scale energy regulation characteristics for NZCP with a larger energy supply scale.

New model of PEV parking lots in the presence of demand response uncertainties

In the presence of today’s environmental concerns, electric transportation services provide significant role of presenting secure and sustainable energy schemes. For this issue, plug-in electric vehicles (PEVs) are an unavoidable part of future smart grids which can provide clear and secure power for different types of electrical energy consumers. In this case, PEVs-based demand response schemes present positive effects of supplying electric powers through smart grid consumers. In the case of evaluating PEV uncertainties, this paper presents a new demand response scheme considering the optimal performance of PEV parking lots through two different reserve markets and electrical energy systems. For this issue, based on the concepts of motivation-based and cost-based demand response programs (DRPs), an adaptive mathematical-based model is developed which considering different time steps, the optimal performance of PEV parking lots are evaluated. Also, based on the performance of several DRPs, the operational performance of PEVs are investigated accurately in which proper participation values with respect to each DRP scheme is estimated. In the case of electricity market uncertainties within different time durations, by using a stochastic-based programming structure, the stochastic performance of PEV parking lots are developed through optimization mathematical model. Numerical results indicate the effectiveness of the proposed approach for optimizing PEV parking lots through different DRPs with considerable effect through supplying sustainable energy.

Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning

Generation expansion planning is a classical problem that determines an optimal investment plan for the expansion of electricity network. With the advent of demand response as a reserved capacity in smart power systems, recent versions of this class of problems model demand response as an alternative for the expansion of the network. This adds uncertainties, since the availability of this resource is not known at the planning phase. In this paper, we model demand response uncertainty in a multi-commodity energy model, called ETEM, to address the generation expansion planning problem. The resulting model takes the form of an intractable multi-period adjustable robust problem which can be conservatively approximated using affine decision rules. To tackle instances of realistic size, we propose a Benders decomposition that exploits valid inequalities and favors Pareto robustly optimal solutions at each iteration. The performance of our new robust ETEM is evaluated in a realistic case study that surveys the energy system of the Swiss “Arc Lémanique” region. Results show that an adjustable robust strategy can potentially reduce the expected cost of the system by as much as 33% compared to a deterministic approach when accounting for electricity shortage penalties. Moreover, an adjustable procurement strategy can be responsible for a 9 billion Swiss francs cost reduction compared to a naive static robust strategy. The proposed decomposition scheme improves the run time of the solution algorithm by 40% compared to the traditional Benders decomposition. To conclude, we provide a discussion on other possible problem formulations and implementations.

Day-ahead Optimal Economic Dispatch of ParkIntegrated Energy System Considering Uncertainty of Demand Response

Day-ahead Optimal Economic Dispatch of ParkIntegrated Energy System Considering Uncertainty of Demand Response

Multi-time scale optimal scheduling model for active distribution grid with desalination loads considering uncertainty of demand response

In the context of the current development of active distribution grids, flexible loads including seawater desalination have been introduced into the distribution grid as important resources in order to improve the ability of source-grid-load interaction. This paper focuses on the seawater desalination loads, and comprehensively considers the demand response uncertainty of desalination load caused by sudden external factors such as residential water consumption and reservoir water volume on the day of the demand response, as well as the characteristic due to the errors of short-term renewable energy forecasting. Afterwards, a multi-time scale optimal scheduling model that takes into account the uncertainty of demand response is proposed, and the risk cost of demand response uncertainty is analyzed and modeled. Besides, this paper introduces the source-grid-load interaction indicator into the optimization model to quantify the interaction effect, and uses the optimization algorithm to analyze examples in order to verify the effectiveness of the model, so as to provide technical support for enhancing the participation of seawater desalination in the source-grid-load interaction of the distribution network.

Flexibility-constraint integrated resource planning framework considering demand and supply side uncertainties with high dimensional dependencies

The propensity to utilization of inexpensive, clean and distributed energy resources in the today's power systems structure are expanding due to various economic, political and sociological reasons. However, the power generated by these resources is extremely unpredictable, which is called uncertainty and exceptionally fluctuated drastically that is known as variability, during the operation epoch. So, exposure to these two features is great significance and value. This problem is known as the power system flexibility in the literature of electrical power system studies. Promoting flexibility in the system is too expensive and can be evaluated in different time frames. This paper studies the power system flexibility with the presence of uncertainty and variability of demand and supply side resources in a planning timeframe. This paper proposed an improved method for demand response uncertainty modeling with Z-number. This method is a hybrid possibilistic and probabilistic process. Furthermore, this paper applied an approved method for renewable generation uncertainty modeling with Monte Carlo. The scrutinies show, the most cost-effective way to consider the flexibility is planning timeframe, so this study proposed a novel paradigm for Flexibility-Constrained Integrated resource planning in presence of high penetration uncertain and variable demand-side and supply-side resources namely as FCIRPU/V-DSRs. The simulations are implemented with the proposed FCIRPU/V-DSRs methodology on a test system. The results show that this innovative framework provides a significant improvement in the power system flexibility at an inferior cost than other investigated methods.

Modeling and Investigation of Demand Response Uncertainty on Reliability Assessment

Demand Response (DR) provides an opportunity for customers to reduce their loads during times of high prices and therefore to shave the peak loads. The power outputs of large-scale generator units can be predicted and controlled easily; therefore, the pricing and reliability of conventional power utilities can be assessed straightforwardly. However, the customer loads are very variable and difficult to predict and control; therefore, the integration of DR might cause uncertainty issues on pricing and reliability and is essential to be further investigated. A novel uncertainty model for load reduction is proposed in this paper. The probability intensities of load reduction are first estimated from the measured load reduction variations. A multi-state probability model is then proposed for load reduction and the Markov process is used to calculate the state probabilities. A stochastic analysis scheme using Monte Carlo simulation for pricing and reliability taking the DR uncertainty into account is then investigated. Several cases are designed to compare the effects of DR uncertainty. Simulation results show that the proposed uncertainty model can be integrated into conventional economic dispatch to precisely evaluate the DR uncertainty on system operation and reliability.

Optimal Operation Analysis of Integrated Community Energy System Considering the Uncertainty of Demand Response

Propose a demand response uncertainty model based on price incentives, describe the relationship between the incentive price and the demand response coefficient. Constructed energy coupling matrices for integrated community energy systems considering demand response based on traditional energy hub models. The uncertainty of the demand response is depicted using the interval approach. Considering the load characteristics and various constraints of the integrated community energy system, optimized operating model with the goal of minimizing operating costs. Using an example of an integrated community energy system for a campus, considering multiple operating scenarios to investigate the effect of integrated demand response under different approaches, the impact of price incentives and incentive schemes on integrated community energy systems. The results show that compared with the traditional method, this method effectively reduces system operating costs and improves the load profile, which helps to achieve a win-win situation for both energy companies and users.

Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling

As the electricity consumption and controllability of residential consumers are gradually increasing, demand response (DR) potentials of residential consumers are increasing among the demand side resources. Since the electricity consumption level of individual households is low, residents’ flexible load resources can participate in demand side bidding through the integration of load aggregator (LA). However, there is uncertainty in residential consumers’ participation in DR. The LA has to face the risk that residents may refuse to participate in DR. In addition, demand side competition mechanism requires the LA to formulate reasonable bidding strategies to obtain the maximum profit. Accordingly, this paper focuses on how the LA formulate the optimal bidding strategy considering the uncertainty of residents’ participation in DR. Firstly, the physical models of flexible loads are established to evaluate the ideal DR potential. On this basis, to quantify the uncertainty of the residential consumers, this paper uses a fuzzy system to construct a model to evaluate the residents’ willingness to participate in DR. Then, based on the queuing method, a bidding decision-making model considering the uncertainty is constructed to maximize the LA’s income. Finally, based on a case simulation of a residential community, the results show that compared with the conventional bidding strategy, the optimal bidding model considering the residents’ willingness can reduce the response cost of the LA and increase the LA’s income.

Optimal Economic Dispatch for an Industrial Park With Consideration of an Elastic Energy Cloud Model With Integrated Demand Response Uncertainty

Under China’s vigorous development of integrated energy services, the Integrated Energy Service Agency (IESA) is responsible for purchasing energy from external markets and selling energy to multi-energy users (MEUs). Currently, an increase in the various forms of energy in industrial parks has caused great uncertainty for MEUs participating in an integrated demand response (IDR) but has also provided an opportunity for industrial parks to optimize energy conservation. Therefore, determining how to build an elastic energy cloud model with IDR uncertainty and add the uncertainty and randomness of the cloud model to the optimal scheduling of an industrial park integrated energy system is a key problem. In this paper, an optimal economic dispatch model of an industrial park is proposed and considers the uncertain elastic energy of IDR. In this model, the IESA is responsible for the reasonable scheduling of equipment for optimal operation, the establishment of integrated energy retail prices for MEUs, and the goal of maximizing the net income of the IESA. First, the functional relationship among the self-elastic coefficient, retail energy prices, and IDR variation is considered. A cloud model of the self-elastic coefficient is constructed to indirectly represent the multiple uncertainties of the elastic energy in the industrial park. Second, this paper compares and analyzes the economic benefits and IDR potential of the industrial park by considering only single power users in different intervals and the selection of cloud drop elements of MEUs in all intervals. Finally, a new scene random sampling method based on interval contributions (SRS-IC) is employed to solve the optimization model, and a typical example is used to demonstrate that the model and method can guarantee the overall economy of the industrial park, improve the computational efficiency, and explore the IDR potential of MEUs.

Cooperative Scheduling for Integrated Electricity and Natural Gas Systems Considering Gas Flow Transient Characteristics and Source–Load Uncertainties

With the continuous expansion of the installation capacity of wind turbines and gas‐fired units, the interdependency between power systems and natural gas systems has increasingly intensified. To deal with wind power uncertainty, an interval optimization‐based cooperative scheduling model for integrated electricity and natural gas systems is proposed, in which natural gas transient flow characteristics are modeled by partial differential equations and transformed by the Wendroff difference scheme and linearization technique. Moreover, demand response uncertainty for power and residential gas loads is considered. Then the uncertainties of wind power generation and power and gas loads, can be represented by interval numbers, and the corresponding objective function and operation constraints can be transformed to the deterministic forms for solving. Two case studies are performed on a 6‐bus power system coupled with 6‐node gas network and the modified IEEE 118‐bus system with 10‐node gas network to demonstrate the effectiveness of the proposed model. Furthermore, the robust optimization model is implemented for comparison. Simulation results demonstrate that considering natural gas transient flow and demand response uncertainty can significantly improve the economic performance of the solution. In addition, the proposed model can reduce the conservativeness of decision‐making by the robust optimization model.

A Day-ahead and Day-in Decision Model Considering the Uncertainty of Multiple Kinds of Demand Response

The uncertainty of demand response (DR) will affect the economics of power grid dispatch due to the randomness of participating users’ intentions. According to the different working mechanisms of price-based demand response (PBDR) and incentive-based demand response (IBDR), the uncertainty models of two types of DR were established, respectively. Firstly, the fuzzy variable was used to describe the load change in PBDR, and the robust optimization theory was used to establish the uncertain set of the actual interruption of the interruptible load (IL). Secondly, according to the different acting speed of the two types of DR, they were deployed in the two-stage scheduling model with other output resources; then based on the fuzzy chance constrained programming theory and multi-stage robust optimization theory, the dispatch problem was transformed and solved by the bat algorithm (BA) and the entropy weighting method. Consequently, intraday running costs decrease with increasing confidence of day-ahead, but increase with day-in reliability, and the economics of the model were verified in the improved IEEE33 node system.