Search

Due to the spatially and temporally transferable workloads, data centers (DCs) become increasingly important in demand response (DR) implementations. By making full use of their owned DCs, data center operators (DCOs) are significant DR resource providers. To fully exploit the DR capability of the DCO on different time scales, we present a two-stage scheduling model for DCOs and the system operator (SO). In the DR scheduling, the SO formulates DR compensation prices first, and then each DCO decides its best-response power demands accordingly. Considering the profit-hunting property of the SO and DCOs, a two-stage DR Stackelberg game model is proposed. Furthermore, the existence and uniqueness of the Stackelberg equilibrium are proved. Finally, to protect the data privacy of DCOs, we design a Kriging-metamodel-based algorithm which avoids the DCO privacy exposure to the SO in the optimization process. Simulation results prove the accuracy and the calculation efficiency of the proposed Kriging-metamodel-based algorithm. Note to Practitioners—DCs play an increasingly important role in the power balance of grids in recent years. Existing studies focusing DCs’ DR participation generally consider the single-stage DR participation of DCs which ignores the role of DCOs in unified scheduling and management of their owned DCs. Considering the impacts of service request submitting time on the DR capabilities of DCs, this work proposes a novel two-stage DR scheduling model in the account of the unified scheduling role of DCs. Then, a Stackelberg game model is further proposed to characterize the profit-hunting property of the SO and DCOs. Finally, a privacy-protected algorithm is designed to address the optimization problem without explosions of DCO’s sensitive data. Simulation results show the effectiveness of the proposed model and algorithm in encouraging DCOs to adjust their energy consumption plans according to the SO’s request. Moreover, it is proven that the proposed algorithm can achieve global optimization with effective data privacy protection and low computation cost. Simulation results suggest that the proposed method can obtain a high-quality solution for a Stackelberg game model with privacy protection.

Security-constrained unit commitment (SCUC) is one of the most fundamental optimization problems in power systems. The objective of SCUC is to minimize the operating cost while respecting both system-wide and generator-specific constraints. It leads to a large-scale and mixed-integer programming (MIP) model with a large number of binary decision variables which is difficult to solve. This paper, based on the convex hull theory of single-unit, proposes a linearization method for the hydro-thermal SCUC problem with decoupled thermal units and variable-head hydro units. Then, the strategy of embedding two types of convex hulls in a multi-unit commitment and the heuristic method of constructing a feasible solution are designed, by which the multi-UC is approximated from large-scale mixed-integer programming to linear programming that can be solved in polynomial time. Finally, we theoretically prove that the optimal solution of the proposed LP model is always better than that of the Lagrangian Relaxation model. Numerical experiments on several large-scale test systems demonstrate the effectiveness and efficiency of the proposed method. Note to Practitioners— This paper proposes a linear programming model for the SCUC problem by lifting up to a higher-dimensional space. It realizes an important innovation in reducing the computational complexity of SCUC from the perspective of linearization. The proposed method can be well applied to large-scale long-term unit commitment problems. To better use this method, the following two properties should be highlighted: 1) the error of the proposed method is less than the Lagrangian relaxation method and decreases with the increasing system scales and 2) the computational efficiency of the proposed method is 10-100 times faster than that of the MIP model. We have tested many practical power systems and find that the error of the proposed LP model is usually very small compared with the precise MIP while the computational performance is significantly improved. In some practical cases, the decision makers usually do not want to find the precise optimal solution while only an approximation under a fast speed, because the boundary condition is imprecise. The proposed method is useful. Besides, for the cases that need the precise optimal solution, the proposed method can provide a high-quality initial solution for the MIP model to accelerate the convergence.

This paper proposed sufficient conditions of the zero duality gap for the second-order conic programming approach of the DC optimal power flow that quadratically embeds the transmission loss. The mechanism of the changes for locational marginal price with differently selecting the slack bus is revisited, based on which a load-weighted distributed slack bus method is introduced. Furthermore, a second-order conic programming-based convexification method is proposed by employing the duality analysis. A favorable property of the proposed method is that mild sufficient conditions of the zero duality gap can be derived by the analysis of Karush-Kuhn-Tucker conditions. Numerical results illustrate the effectiveness of the proposed method and physically prove the proposed sufficient conditions. Compared with traditional and similar market-clearing methods, the proposed method has better performance on convergence, robustness, and accuracy. Note to Practitioners—This paper was motivated by the problem of the pricing mechanism in the deregulated electricity market but it is also applicable to rapidly solving the power flow of power systems with sufficient reactive power. Existing approaches use the B-coefficient to estimate the transmission loss in the model non-linearly. In this paper, an improved approach considering transmission loss is proposed, which further introduces the distributed slack bus method and subsequently employs the second-order cone programming algorithm. The proposed method hedges the risk of appearing multiple solutions or lack of accuracy. Rigorous mathematical proofs are derived to support the method. For industrial practice, three sufficient conditions suitable for different kinds of power systems are given, indicating when the proposed approach can be efficiently applied. The proposed method can be integrated into the existing electricity market trading platform for independent system operators and facilitates real-time market clearing and electricity price calculation. Future research is expected to improve the accuracy of the model based on nodal power balance equations.

Large-scale multi-scenario stochastic economic dispatch (SED) is hard to directly solve due to the huge number of variables and constraints. To reduce the computational burden, a nested dual-DQAM (Diagonal Quadratic Approximation Method) is proposed in this paper to decouple the SED problem in both scenarios and time periods, where each subproblem only contains one time period and one scenario. Moreover, these subproblems can be handled in parallel, such that the computational performance can be significantly improved. Besides, we have investigated the optimal policy to select the best parallel structure of the proposed dual-DQAM, and the theorical convergence performance is proved. Numerical results on several test systems show the effectiveness of the proposed dual-DQAM. Note to Practitioners—Nowadays, the safe operation and economic dispatch in power system are greatly challenged by the high penetration of renewable energy. Although the uncertainty of renewable energy can be well modeled by stochastic programming, the solution complexity will seriously increase due to the large number of typical scenarios. For this problem, the parallel solution using decomposition methods is the state-of-the-art strategy. In this paper, we propose an efficient solution to the multi-scenario SED problem by temporal and scenario decompositions. It realizes an important innovation in both reducing the computational burden and improving the solving efficiency, which greatly promotes the application of SED in large-scale systems. To better use this method, the following two properties should be highlighted: i) the proposed method has a convergence guarantee and works especially well on SED due to the sparsity property of linking constraint matrices. ii) the computational efficiency of the proposed method becomes more significant as the number of time periods and scenarios grows and can be further improved under a fast ramp rate.

Household-level distributed energy sources, such as rooftop photovoltaic, microturbines, and energy storage, become important behind-the-meter (BTM) resources. BTM resources can meet all or part of users’ demands. Establishing a peer-to-peer (P2P) energy trading network among these users will further promote the utilization of BTM resources. However, such a proposal faces the problem that cyber trading and physical dispatching are difficult to coordinate. Therefore, this paper proposes the architecture of the behind-the-meter peer-to-peer (BTM-P2P) energy trading system, including the cyber layer and physical layer. To ensure that users strictly execute the cyber trading results in the physical layer, a trading mechanism considering credit is designed, and the user’s credit has an impact on the bidding priority, which in turn urges the user to strictly execute in subsequent tradings. Then, a light blockchain suitable for energy trading is developed, ensuring that both the trading results and the actual dispatching results are not tampered with. Database technology is inserted in the light blockchain to improve efficiency. Finally, a BTM-P2P cyber-physical testbed coupling physical dispatching with cyber trading is built, providing technical support for the implementation of BTM-P2P.

High proportion of energy storage systems (ESSs) and flexible loads signify the main features of a modern power system. ESS with its bi-directional flow characteristic can flexibly change power network operations, thus providing a new solution for voltage regulation and control. However, since ESS resources are dispersed throughout the power system, it is necessary to design an effective aggregation scheme to achieve a concise voltage regulation. In this paper, a novel sponge grid is proposed, which is capable of both local and global tasks to offer greater flexibility and initiative in power system operations. On the one hand, it constructs the source and forms virtual energy storage (VES) systems to satisfy local demands. On the other hand, the sponge grid applies the superposition of a large number of VES systems spontaneous effects to present a collective voltage regulation behavior. Then, a message passing-based decentralized algorithm is proposed and designed for the local voltage regulation in the sponge grid. Various constraints including those representing inequality boundary, state of charge, and complex line flows, are expressed analytically and computed fast by the proposed portable projection approach. The proposed sponge grid model is tested on a modified IEEE 33-bus system to verify its effectiveness and superior performance.

This paper presents a loop analysis and angle recovery (LAAR) based reactive power optimization for three-phase unbalanced and weakly-meshed active distribution networks (ADNs). For each loop, the width first search (WFS) method is used to find the breakpoint bus of each tie line, and then all loops are broken up at these breakpoint buses by disconnecting tie lines, placing added buses, and adding compensation powers, so that the original weakly-meshed ADN can be precisely converted into an equivalent radial ADN. Furthermore, the traditional second-order cone programming can be employed for the equivalent radial networks to find the global optimal solution. However, the compensation powers are not constant values but related to the bus voltages, which are unknown before opening the loops. To address this problem, we design an iterative method to dynamically update the values of compensation powers until the convergence criterion is met. Moreover, the voltage angles are recovered for all loops at each iteration. The effectiveness of the proposed LAAR method is demonstrated by 9 cases, and the results show that the LAAR method can achieve a better convergence performance than the traditional method.

Carbon emission rights (CER) are a market mechanism adopted to reduce carbon dioxide emissions. In this paper, a decentralized market model integrating electricity and CER trading is established for a microgrid. The proposed trading model not only satisfies the demand for transactions but also ensures the constraint of total carbon emissions for the microgrid. Energy storage (ES) is introduced to balance loads more economically, and the constraint specifying that the ES cannot be simultaneously charged and discharged is proven to be satisfied automatically, ensuring the convexity of the model. Furthermore, by inserting local trackers to global constraints for each node, a scalable fully distributed algorithm is designed to solve the model locally for both global equality and inequality constraints. The proposed algorithm decomposes the arithmetic demand to each user without intermediate agents, which can effectively reduce the cost and ensure the transparency of trading. The algorithm is also proven to be convergent. Numerical results verify the effectiveness of the proposed market model.