In a deregulated market, wholesale energy costs and distribution investment costs contribute significantly to consumers' electricity bills. However, in a low carbon electrical power system, the two cost pressure points may not be synchronous in time and space with each other. This paper develops a novel methodology for home area energy management as a key vehicle for demand response, using electricity storage devices. The aim is to enable energy storage at consumer premises to not only take advantage of lower wholesale energy prices, but also to support low voltage (LV) distribution networks for reducing network investment. New operation strategies for domestic energy storage to facilitate demand response (DR) are developed in the paper. They have the capability to maximize the overall savings in energy costs and investment costs. In the proposed approach, the operation of home-area energy storage devices is jointly conducted by end customers and network operators. The purpose is to fight for an optimal balance between DRs to energy price and to network congestion, and thus to maximize benefits for both consumers and network operators. An intensive study is carried out to investigate the impacts of different dispatch strategies on wholesale energy costs and network investment costs. Benefit quantification methods are introduced as well to evaluate the total benefits in terms of savings in energy costs and investment costs that can be brought along by the proposed operation approach. The demonstration is carried out on two practical distribution networks with varying utilization levels for one typical calendar day and a whole year.

Energy hub system (EHS) incorporating multiple energy carriers, storage and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation.

The increasing coupling between natural gas and electricity systems by gas-fired generation units brings new challenges to system analysis, such as pressure variations due to consumption perturbations of generation units. The emerging issues require revolutionary modeling and analysis techniques. This paper proposes a novel model to quantify gas pressure variations due to gas-fired power unit ramping and the impact of constraints from natural gas pressure change on ramp rates of gas-fired plants. By utilizing Laplace transform to resolve the governing equations of gas networks, the proposed model can significantly reduce modeling complexity and computational burden. The dynamic behaviors in time scale in s-domain and spatial partial differential equations are transformed into finite difference equations. By introducing the concept of transfer matrices, the relation between states at each node of gas systems can be expressed by transfer parameter matrices. Additionally, a simplified model is introduced to simply the analysis. The explicit expressions of nodal pressure variations in response to demand change are very convenient for analyzing system dynamic performance under disturbances, identifying the most influential factors. The new models are extensively demonstrated on three natural gas networks and benchmarked with traditional simulation approaches. Results illustrate that they produce very close results with the simulation approach, particularly when gas pipelines are long and enter steady states.

As a clean energy, wind power has been extensively exploited in the past few years. However, oscillations in wind turbines, particularly those from controllers, could severely affect the stability of power systems. Therefore, oscillation suppression is a recent research focus. Based on the small signal model eigenvalues and participation factors, this paper detects the sub-synchronous interactions (SSI) mainly determined by converters' PI parameters in a grid-connected doubly fed induction generator (DFIG). With the aim of oscillation restraint, a novel optimization model with the reference-point based Non-Dominated Sorting Genetic Algorithm (NSGA-III) and the t-distributed stochastic neighbour embedding (t-SNE) is developed to explore and visualize optimal ranges of PI parameters, facilitating the selection of the appropriate PI parameters to augment the damping. Additionally, to study the adaptability of the optimal PI parameters, interactions performance of the system that uses optimal parameters is studied with different output levels of the wind turbine. Finally, a time domain simulation and a practical experiment are conducted to demonstrate the effectiveness of the proposed approach. Results illustrate that the SSI of a grid-connected DFIG is suppressed by the optimization model. This study is highly beneficial to power system operators in integrating wind power and maintaining system stability.

The energy hub is a powerful conceptualization of how to acquire, convert, and distribute energy resources in the smart city. However, uncertainties such as intermittent renewable energy injection present challenges to energy hub optimization. This paper solves the optimal energy flow of adjacent energy hubs to minimize the energy costs by utilizing the flexibility of energy resources in a smart city with uncertain renewable generation. It innovatively models the power and gas flows between hubs using chance constraints, thus permitting the temporary overloading acceptable on real energy networks. This novelty not only ensures system security but also helps reduce or defer network investment. By restricting the probability of chance constraints over a specific level, the energy hub optimization is formulated as a multiperiod stochastic problem with the total generation cost as the objective. Cornish-Fisher expansion is utilized to incorporate the chance constraints into the optimization, which transforms the stochastic problem into a deterministic problem. The interior-point method is then applied to resolve the developed model. The proposed chance-constrained optimization is demonstrated on a three-hub system and results extensively illustrate the impact of chance constraints on power and gas flows. This work can benefit energy hub operators by maximizing renewable energy penetration at the lowest cost in a smart city.