With the rapid increase in distributed generation (DG), the issue of voltage regulation in the distribution network becomes more significant and centralized voltage control (or active network management) is one of the proposed methods. Alternative work on intelligent distributed voltage and reactive power control of DG has also demonstrated benefits in terms of the minimization of voltage variation and violations as well as the ability to connect larger generators to the distribution network. This paper uses optimal power flow to compare the two methods and shows that intelligent distributed voltage and reactive power control of the DG gives similar results to those obtained by centralized management in terms of the potential for connecting increased capacities within existing networks.

More effective energy production requires a greater penetration of storage technologies. This paper takes a looks at and compares the landscape of energy storage devices. Solutions across four categories of storage, namely: mechanical, chemical, electromagnetic and thermal storage are compared on the basis of energy/power density, specific energy/power, efficiency, lifespan, cycle life, self-discharge rates, capital energy/power costs, scale, application, technical maturity as well as environmental impact. It's noted that virtually every storage technology is seeing improvements. This paper provides an overview of some of the problems with existing storage systems and identifies some key technologies that hold promise.

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A bottom-up modeling approach is presented that uses a Markov chain Monte Carlo (MCMC) method to develop demand profiles. The demand profiles are combined with the electrical characteristics of the appliance to create detailed time-varying models of residential loads suitable for the analysis of smart grid applications and low-voltage (LV) demand-side management. The results obtained demonstrate significant temporal variations in the electrical characteristics of LV customers that are not captured by existing load profile or load model development approaches. The software developed within this work is made freely available for use by the community.

Domestic heating has a large share in the UK total energy consumption and significant contribution to the greenhouse gas emissions since it is mainly fulfilled by fossil fuels. Therefore, decarbonising the heating system is essential and an option to achieve this is by heating system electrification through heat pumps (HP) installation in combination with renewable power generation. Potential increase in performance and flexibility can be achieved by pairing HP with thermal energy storage (TES), which allows the shifting of heat demand to off peak periods or periods with surplus renewable electricity. We present a design and operational optimisation model which is able to assess the performance of HP-TES relative to conventional heating system. The optimisation is performed on a synthetic heat demand model which requires only the annual heat demand, temperature and occupancy profiles. The results show that the equipment and operational cost of a HP system without TES are significantly higher than for a conventional system. However, the integration of TES and time-of-use tariffs reduce the operational cost of the HP systems and in combination with the Renewable Heating Incentive make the HP systems cost competitive with conventional systems. The presented demand model and optimisation procedure will enable the design of low carbon district heating systems which integrate the heating system with the variable renewable electricity supply.

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h i g h l i g h t sWe provide a model for prioritization of energy efficiency measures in buildings. We examine the case of a new building and one under renovation. Objective functions are total primary energy consumption and total investment cost. We provide a software tool that solves this multi-objective optimization problem. Primary energy consumption and investment cost are inversely proportional. a b s t r a c t Buildings are responsible for some 40% of the total final energy consumption in the European Union and about 40% of the world's primary energy consumption. Hence, the reduction of primary energy consumption is important for the overall energy chain. The scope of the current work is to assess the energy efficiency measures in the residential and small commercial sector and to develop a methodology and a software tool for their optimal prioritization.The criteria used for the prioritization of energy efficiency measures in this article are the primary energy consumption and the initial investment cost. The developed methodology used is generic and could be implemented in the case of a new building or retrofitting an existing building. A multi-objective mixed-integer non-linear problem (MINLP) needs to be solved and the weighted sum method is used. Moreover, the novelty of this work is that a software tool has been developed using 'Matlab Ò ' which is generic, very simple and time efficient and can be used by a Decision Maker (DM). Two case studies have been developed, one for a new building and one for retrofitting an existing one, in two cities with different climate characteristics. The building was placed in Edinburgh in the UK and Athens in Greece and the analysis showed that the primary energy consumption and the initial investment cost are inversely proportional.

This paper describes a fully coupled, wave-to-wire time-domain model that can simulate the hydrodynamic, mechanical, and electrical response of an array of wave energy converters. Arrays of any configuration can be simulated to explore both the effects of the array on the electricity network and of network events on the devices within the array. State-space modeling of the hydrodynamic radiation forces enables fast and accurate prediction of the interacting response of multiple devices, including the effects of wave climate, control strategies, and network power flow. Case studies include the demonstration of the bidirectional interaction of the array and the network.