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Energy flow analysis is a fundamental tool to determine the network states of the integrated energy systems (IES). For the widely deployed IES with coupled power grids (PG) and heating networks (HN), we still lack a generic tool that can be easily employed to calculate the quasi-dynamic energy flows of it. In this paper, we have developed a generic code package (named as MATHN) for energy flow analysis of the quality-regulated HN. After that, a generic solution framework that leverages MATPOWER and MATHN to realize decomposed energy flow calculation of PG and HN is proposed. Behind the MATHN tool, a model reformulation technique is proposed to eliminate the massive indirect variables of the basic HN model discretized by finite difference, which finally derives a compact matrix formulation (similar to the network equation of power grid: $\boldsymbol {I}=\boldsymbol {Y}\boldsymbol {U}$ ) for generic description of any HN and also slashes the original model scale by around 20 times. Moreover, the solution strategy behind the MATHN tool further converts this matrix formulation into a standard system of non-homogeneous linear equations, with which some existing well-recognized algorithms can be directly applied for efficient and accurate solution. Case studies on three different scales of test systems demonstrate the generality, efficiency and accuracy of our methods. All the codes and input data are packaged into the MATHN tool and are made open-source for non-commercial use.

With the global trend to exploit more renewable energy sources, tidal range energy has been gaining more attention recently. This power generation technology is expected to reduce the share of fossil fuels and provide flexibility to the power system. With a pioneering tidal range generation project just granted in Wales and more proposals planned in Great Britain, it is important to study how the incorporation of multiple tidal range power stations will affect power system operation. In this paper, the role of tidal range energy generation in the future Great Britain power system is investigated based on a day-ahead scheduling model of power system incorporating multiple tidal range power stations. In the proposed model, tidal range power stations situated at different sites operate flexibly and in coordination, supporting the power system to reach the minimum operating cost. A case study based on the Great Britain electricity transmission system in 2030 with one tidal barrage and one tidal lagoon was investigated. The results showed that the coordination of flexible tidal range power stations can reduce the power system's operating cost. Furthermore, the energy-storage feature of tidal range power stations can act as a stable source of flexibility in the power system.

Electric power systems are transitioning towards a decentralized paradigm with the engagement of active prosumers (both producers and consumers) through using distributed multi-energy sources. This paper proposes a novel Blockchain based peer-to-peer trading architecture which integrates negotiation-based auction and pricing mechanisms in local electricity markets, through automating, standardizing, and self-enforcing trading procedures using smart contracts. The negotiation of the volume and price of the peer-to-peer electricity trading among prosumers is modeled as a cooperative game, and the interaction between a retailer and its ensemble of prosumers is modeled as a Stackelberg game. The flexibility provision from residential heating systems is incorporated into the energy scheduling of prosumers. Case studies demonstrate that the proposed architecture in local electricity markets helps improve local energy balance. Flexibility from the residential heating systems enables prosumers to be more responsive to the variation of retail electricity prices. The proposed model reduces 41.24% of average daily electricity costs for individual prosumers or consumers compared to the case without the peer-to-peer electricity trading.

This paper develops a new approach for producing probabilistic wind power forecasts using a single forecast. The Singular Spectrum Analysis technique is used as the forecasting technique. Given the confidence interval calculated for the single forecast, a large number of random forecasts were generated through the Monte Carlo method. The purpose of generating probabilistic wind power forecasts is to use the results in a stochastic programming unit commitment problem. Therefore, probabilistic forecasts are reduced to a small number of representative forecast scenarios by applying a scenario reduction algorithm. The stability of the scenario reduction algorithm is also evaluated. The results indicate that the insignificant changes of operational cost of the stochastic programming problem reflect a deletion of unimportant forecast scenarios.

There is a need for major greenhouse gas emission reductions from heating in order to meet global decarbonisation goals. Electricity is expected to meet much of the heat demand currently provided by fossil fuels in the future and heat pumps may have an important role. This electrification transformation is not without challenges. Through a detailed narrative review alongside expert elicitation, we propose four principles for heat decarbonisation via electrification: putting energy efficiency first, valuing heat as a flexible load, understanding the emission impacts of heat electrification and designing electricity tariffs to reward flexibility. As a route to heat decarbonisation, when combined, these principles can offer significant consumer and carbon reduction benefits. In the short term these principles can encourage the smooth integration of heat electrification and in the longer term these principles are expected to reduce the scale of required infrastructural expansion. We propose a number of policy mechanisms which can be used to support these principles including (building) regulation, financial support, carbon standards, energy efficiency obligations and pricing.