Incentive-based Demand Response

This formulation and critical assessment of a novel type of demand response (DR) program targeting retail customers (such as smallmedium size commercial, industrial, and residential customers) who are equipped with smart meters yet still face a flat rate are considered. Enabled by pervasive mobile communication capabilities and smart grid technologies, load serving entities (LSEs) could offer retail customers coupon incentives via near-real-time information networks to induce demand response for a future period of time in anticipation of intermittent generation ramping andor price spikes. This scheme is referred to as coupon incentive-based demand response (CIDR). In contrast to the real-time pricing or peak load pricing DR programs, CIDR continues to offer a flat rate to retail customers and also provides them with voluntary incentives to induce demand response. Theoretical analysis shows the benefits of the proposed scheme in terms of social welfare, consumer surplus, LSE profit, the robustness of the retail electricity rate, and readiness for implementation. The pros and cons are discussed in comparison with existing DR programs.

Figure. The three-layer information exchange structure for CIDR implementation.

References

• Haiwang Zhong, Le Xie, Qing Xia. Coupon Incentive-based Demand Response: Theory and Case Study. IEEE Transactions on Power Systems,2013,28(2):1266-1276 (ESI Highly Cited Paper Top 1%)

• Haiwang Zhong, Le Xie, Qing Xia. Coupon Incentive-based Demand Response in Smart Grid. 2012 IEEE Power and Energy Society General Meeting, 22 - 26 July 2012, San Diego, CA, United States, July, 2012

• Haiwang Zhong, Le Xie, Qing Xia, Chongqing Kang, and Saifur Rahman. Multi-stage Coupon Incentive-based Demand Response in Two-Settlement Electricity Markets. IEEE Power & Energy Society ISGT Conference, 2015. Washington DC, U.S., February, 2015

Distributed Demand Response

The real-time demand response (DR) framework and model for a smart distribution grid is formulated. The model is optimized in a distributed manner with the Lagrangian relaxation (LR) method. Consumers adjust their own hourly load level in response to real-time prices (RTP) of electricity to maximize their utility. Because the convergence performance of existing distributed algorithms highly relies on the selection of the iteration step size and search direction, a novel approach termed Lagrangian multiplier optimal selection (LMOS) is proposed to overcome this difficulty. Via sensitivity analysis, the energy demand elasticity of consumers can be effectively estimated. Then the LMOS model can be established to optimize the Lagrangian multipliers in a relatively small linearized neighborhood. The salient feature of LMOS is its capability to optimally determine the Lagrangian multipliers during each iteration, which greatly improves the convergence performance of the distributed algorithm.

Figure. The framework of real-time DR.

References

• Jianxiao Wang, Haiwang Zhong, Xiaowen Lai, Qing Xia, Chang Shu, Chongqing Kang. Distributed real-time demand response based on Lagrangian multiplier optimal selection approach. Applied Energy,2017,190(1):949-959

• Yang Bai, Haiwang Zhong, Qing Xia. Real-Time Demand Response Potential Evaluation: A Smart Meter Driven Method. 2016 IEEE Power & Energy Society General Meeting, Article Number: 16PESGM2114, Boston, MA, United States, July, 2016

Integrated Demand Response

Demand response (DR) is a critical and effective measure to stimulate the demand side resources to interact with renewable generation in the power system. However, the conventional scope of DR cannot fully exploit the interaction capabilities of demand side resources, which limits the energy users in the electric power system. With the revolution of the traditional economic and social pattern based on centralized fossil energy consumption, 'Energy Internet’ is impelling the development of the third industrial revolution, which aims at promoting the incorporation of sustainable energy and internet technology, and facilitating the integration of multi-energy systems (MESs). By integrating electricity, thermal energy, natural gas and other forms of energy, the smart energy hub (SEH) makes it possible for energy users to flexibly switch the source of consumed energy. With the complementarity of MESs, even the inelastic loads can actively participate in DR programs, which fully exploits the interaction capability of DR resources while maintaining the consumers’ comfort. This novel vision of the DR programs is termed as 'Integrated Demand Response (IDR)’. In this context, the state-of-the-art of IDR in the MESs is reviewed for the first time. The basic concept of IDR and the value analysis are introduced. The research on IDR in the MES is then summarized. The overviews of the engineering projects around the world are introduced. Finally, the key issues and potential research topics on IDR in the MES are proposed.

Figure. The diagram of integrated demand response.
The synergy among different energy carriers also enable a smart energy hub (SEH) to provide flexibility to multiple energy sectors simultaneously. The SEH refers to a local multi-energy system equipped with distributed energy converters and advanced communication devices. With energy conversion devices, the SEH can serve its fixed terminal loads with adjustable multi-energy inputs. This ability is termed as integrated flexibility. The integrated flexibility will provide the system operator with a higher degree of freedom to optimally schedule the system. The very important issue that has to be addressed is how much flexibility the smart energy hub can provide. Here we define the integrated flexible region (IFR) of an energy hub as the allowable range of its multi-energy inputs subjected to device operating limits and terminal load demand. The IFR is mathematically formulated as the projection of the operating feasible region of the SEH from the state space to the input space. The estimation of the IFR is a polyhedral projection problem, which can be solved by algorithms such as Fourier elimination, block elimination, and the vertex enumeration method. The figure below exhibits the IFR of an energy hub with electricity, natural gas, and heat inputs. As can be seen, the IFR is a closed region in the first quadrant of a 3D coordinate and varies with the terminal loads of the energy hub. A two-stage operation scheme is also proposed to implement the integrated flexibility in the multi-energy system operation.

Figure. The terminal load of the SEH and IFR.

References

• Jianxiao Wang, Haiwang Zhong, Ziming Ma, Qing Xia, Chongqing Kang. Review and prospect of integrated demand response in the multi-energy system. Applied Energy,2017,202():772-782

• Jianxiao Wang, Haiwang Zhong, Qing Xia, Shengchun Yang. A model and method of demand response for thermostatically-controlled loads based on cost-benefit analysis Automation of Electric Power Systems,2016,40(5):45-53 (in Chinese)

• Z. Tan, H. Zhong, Q. Xia, C. Kang and H. Dai, “ Exploiting Integrated Flexibility from a Local Smart Energy Hub” 2020 IEEE Power&Energy Society General Meeting (PESGM), Montreal, Quebec, Canada,2020, pp.1-5

Demand Response in Electricity Market

The electricity capacity market is designed to ensure the adequate availability of necessary resources in the long run. Demand resources (DR) could play an important role in the capacity market. However, since the capacity market is a long-term market, the uncertainties become a major problem for demand response providers’ (DRPs) decision making. This paper first formulates the DRP decision problem considering uncertainties in capacity market clearing prices and active hour of DRs using a min-max model. Then, a scenario approach is introduced to solve the model in comparison with deterministic, stochastic and robust approaches. It is shown through a case study that the scenario approach offers a number of desirable features including (1) adjustable level of robustness and computational efforts; (2) efficient computation based on historical samples; and (3) theoretically bounded risk of violation based on the number of samples.

Figure. Performance and violation probability of different methods.

References

• Dian Chen, Le Xie, Haiwang Zhong, Qing Xia. The Scenario Approach for Demand Response Providers in Capacity Markets. 12th IEEE PES PowerTech Conference, Article Number: 870, Manchester, UK, June, 2017

Demand Response with Machine Learning Technique

Due to information asymmetry, analytical model may fail to keep high performance when some necessary information are absent. A novel perspective is provided to embed neural network (data-driven model) in optimization model (analytical model). The new-style model is then formulated and solved by a hybrid method with dual neural network and successive linear programming. Here, one of the neural network is built up to accelerate the derivative calculation of another network. Demand response aggregation problem is analyzed as an application, where price response feature and incremental model overcome the problem of information asymmetry. Case studies verify the correctness and efficiency of the proposed method, showing the potential for wider applications.

Figure. The framework of hybrid model applied to improve demand response.

References

• Guangchun Ruan, Haiwang Zhong, Qing Xia, Qifeng Huang and Chao Zhou, Embed Neural Network in Optimization Model: An Application of Demand Response Aggregation Under Information Asymmetry, 2019 IEEE PES General Meeting, 4-9 Aug 2019, Atlanta, USA.