Zhimin Wang

Active Demand Response Using Shared Energy Storage for Household Energy Management

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.

Developing trend of domestic electricity tariffs in Great Britain

Household flat rate electricity tariffs have been ongoing for decades in the UK. The fitness of this type of tariffs is challenged in the new smart grid and smart metering environment, where demand responses are expected to play an important role to support the energy markets and the network. Generally speaking, most advanced electricity tariffs to date aim to reduce energy prices in a competitive electricity market. This paper reviews a range of electricity tariffs exercised by major suppliers in the UK and their associated drawbacks. The need for further development in electricity tariffs is analyzed with an aim to support suppliers undergoing innovations to provide various tariffs for different types of customers to maximize their participations.

Active household energy storage management in distribution networks to facilitate demand side response

This paper tries to devise a methodology for managing domestic electric energy consumption with storage devices in distribution networks. Following a brief introduction to the current demand side response (DSR) features and assessment of electricity demand, an analytical approach for shifting demand from peak to off-peak periods is developed by utilizing the household energy storage. A comparative study is thereby carried out to investigate the impacts of different charging and discharging scenarios under three types of wholesale energy prices, i.e., conforming price, volatile price and conflicting price. The benefits of the load shift caused by introducing household storage devices can be reflected as wholesale cost saving for customers and network investment deferral for system operators. A typical UK household load profile is adopted as an example to demonstrate the impact of the proposed energy storage control strategies on the benefits.

Critical peak pricing tariff design for mass consumers in Great Britain

“Critical Peak Pricing(CPP)” refers to a method of pricing electricity whereby Time of Use (TOU)Pricing is in effect with the exception of certain peak periods at which time electric prices may reflect the costs of generating and/or purchasing electricity at the wholesale level[1]. It aims to reduce load during the relatively few, very expensive hours more dynamically. In CPP tariff design, the important elements are the time window over the peak price period and the degree of price differentiations between the peak and off peak times. This paper uses Great Britain market index prices and market index volumes to statistically analyze the price distribution and demand distribution. These analyses will inform the design of appropriate peak pricing window if Great Britain is to move to CPP.

Development of time-of-use price by clustering techniques

Active demand side response (DSR) from domestic customers can generate benefits in terms of reducing energy costs for customers and shaving peak demand for distribution network operators (DNOs). However, real-time price (RTP) is considered to be too dynamic for customers to response. Also, it is infeasible for most energy storage equipment to response to variable signals, such as RTP, as they can only charge/discharge a few cycles throughout a day. Due to these constraints, time-of-use (TOU) price is a more natural price signal for DMS. This paper proposes a novel statistical method to successfully convert RTP to TOU that captures the most significant price variations without comprising too much accuracy in total energy revenue from customers. The proposed method adopts hierarchical clustering techniques to group RTP into clusters, and each settlement period is assigned to one of the clusters to form a TOU pattern. For each cluster of the TOU tariff pattern, the tariff rate is determined by keeping the total customer revenue unchanged.

Distributed storage capacity reservations for residential PV generation utilization and LV network operation

This paper proposes a novel method for distributed generation utilization and Low Voltage (LV) network management with residential energy storage batteries in distribution systems. The batteries installed at end users are actually used for demand response, allowing customers and distributed network operators (DNOs) to reserve part of storage capacity in order to absorb energy from residential photovoltaic (PV) generation and relieve network congestion. The major difficulty in carrying out storage capacity reservations is the quantification of reserved capacity based on not only predicted PV outputs, but also identified network pressures. In order to overcome this difficulty, the algorithm of storage capacity reservation considers both energy and network sides. The reserved storage capacities are basically evaluated following two criteria: energy capacity reserved for cheap energy charging and capacity reservation according to customers contribution to network pressures. A case study is carried out in a practical network in the UK to implement the proposed method, where the benefits are quantified in terms of energy consumption reduction from grid, energy cost reduction and network cost saving.

Cost and emission savings from the deployment of variable electricity tariffs and advanced domestic energy hub storage management

This paper uses the energy hub concept to holistically model future energy infrastructure in domestic buildings, including energy storage. The developed model allows the deployment of a novel bi-criteria optimization algorithm for minimizing both the cost and emissions of energy hub operation whilst taking advantage of dynamic tariffs. Unlike the traditional flat rate tariffs, the dynamic tariffs employed in this paper reflect variations in the wholesale energy market, and are used as commercial inputs to drive the storage operation and reduce both costs and emissions. The developed algorithm and hub model are used to optimize an example energy hub against four 24 hour periods of loads and dynamic tariffs, one from each season. Annual savings are estimated and compared against a base case, with no storage or management, of a typical house in the UK, showing significant cost and emissions savings.