This paper reviews the state of the art of research and industry practice on demand response and the new methodology of transactive energy. Demand response programs incentivize consumers to align their demand with power supply conditions, enhancing power system reliability and economic operation. The design of demand response programs, performance of pilot projects and programs, consumer behaviors, and barriers are discussed. Transactive energy is a variant and a generalized form of demand response in that it manages both the supply and demand sides. It is intended for a changing environment with an increasing number of distributed resources and intelligent devices. It utilizes the flexibility of various generation/load resources to maintain a dynamic balance of supply and demand. These distributed resources are controlled by their owners. However, the design of transaction mechanisms should align the individual behaviors with the interests of the entire system. Transactive energy features real-time, autonomous, and decentralized decision making. The transition from demand response to transactive energy is also discussed.

Power system operation will encounter numerous variabilities as the proliferation of renewable energy continues. Such random events will require the evaluation of corresponding variables and the assessment of their impacts on the monitoring and the control of stochastic power systems. In this paper, a global sensitivity analysis (GSA) method is proposed to perform a priority ranking of renewable energy variabilities that will affect the voltage stability of power systems. First, a probabilistic model for the load margin calculation is presented considering the renewable power generation. Then, GSA is applied to models with correlated random input variables and the importance index is designed for evaluating the impacts of such variables. Next, the stochastic response surface method (SRMS) is adopted in GSA to improve the computation efficiency of the proposed evaluation method. Finally, the overall procedure is presented to identify critical variables that can affect the variability of load margins in voltage stability analyses. The proposed method is applied to power systems with a large penetration of renewable power and the influences of critical variabilities on voltage stability are analyzed. In this paper, the proposed method is tested using the IEEE 9-bus and the IEEE 118-bus systems, and its accuracy is compared with those of the local sensitivity analysis method and the commonly used GSA methods.

In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner. Simulation results of two power system cases are discussed to substantiate the effectiveness and characteristics of the proposed approach.

Distributed optimization algorithms for security-constrained economic dispatch (SCED) problems have been the subject of significant research interest in recent years. However, existing distributed SCED algorithms can be ineffective in the presence of malicious participants and inefficient in the absence of a coordinator. On the other hand, blockchain, an emerging technique known as the trust machine, has not shown its potential to address the above challenges in state-of-the-art literature. This paper proposes a blockchain-based distributed SCED algorithm. Using blockchain to form a coordination committee and enable balance among committee members, the proposed method allows the use of hierarchical SCED algorithms in the absence of a coordinator and can disable malicious participants. Numerical results show the robustness and necessity of the proposed blockchain-based SCED algorithm, by comparing the SCED results with and without blockchain.