Search

Electric power systems are in state of transition as they attempt to evolve to meet new challenges provided by growing environmental concerns, increases in the penetration of distributed renewable energy sources (DRES) as well as the challenges associated with integrating new technologies to enable smart grids. New techniques to improve the electrical power system, including the distribution system, are thus needed. One such technique is dynamic distribution system reconfiguration (DNSR), which involves altering the network topology during operation, providing significant benefits regarding the increased integration of DRES. This paper lays out an improved model which aimed to optimize the system operation in a coordinated way, where DRES, energy storage systems (ESS) and DNSR are considered as well as the uncertainty of these resources. The objective function was modeled to incentivize the uptake of DRES by considering the cost of emissions to incentivize the decarbonization of the power system. Also, the switching costs were modeled to consider not only the switching, but also the cost of degradation of these mechanisms in the system operation. Two systems are used to validate the model, the IEEE 119-bus system, and a real system in São Miguel Island. The results of this paper show that using DNSR, DRES, and ESS can lead to a significant 59% reduction in energy demand through a 24-hour period. In addition, using these technologies results in a healthier, more efficient, and higher quality system. This shows the benefits of using a variety of smart grid technologies in a coordinated manner.

The generation mix of Portugal now contains a significant amount of variable renewable energy sources (RES) and the amount of RES is expected to grow substantially. This has led to concerns being raised regarding the security of the supply of the Portuguese electric system as well as concerns relating to system inertia. Deploying and efficiently using various flexibility options is proposed as a solution to these concerns. Among these flexibility options proposed is the use of battery energy storage systems (BESSs) as well as relaxing system inertia constraints such as the system nonsynchronous penetration (SNSP). This article proposes a stochastic mixed-integer linear programming problem formulation, which examines the effects of deploying BESS in a power system. The model is deployed on a real-world test case and results show that the optimal use of BESS can reduce system costs by as much as 10% relative to a baseline scenario and the costs are reduced further when the SNSP constraint is relaxed. The amount of RES curtailment is also reduced with the increased flexibility of the power system through the use of BESS. Thus, the efficiency of the Portuguese transmission system is greatly increased by the use of flexibility measures, primarily the use of BESS.

The close interaction between the electricity market and the end-users can assist the demand response (DR) aggregator in handling and managing various uncertain parameters simultaneously to reduce their effect on the aggregator's operation. As the DR aggregator's main responsibility is to aggregate the obtained DR from individual consumers and trade it into the wholesale market. Another responsibility of the aggregator is proposing the DR programs (DRPs) to the end-users. This article proposes a model to handle these uncertainties through the development of a novel hybrid stochastic-robust optimization approach that incorporates the uncertainties around wholesale market prices and the participation rate of consumers. The behavior of the consumers engaging in DRPs is addressed through stochastic programming. Additionally, the volatility of the electricity market prices is modeled through a robust optimization method. Two DRPs are considered in this model to include both time-based and incentive-based DRPs, i.e., time-of-use and incentive-based DR program to study three sectors of consumers, namely industrial, commercial, and residential consumers. An energy storage system is also assumed to be operated by the aggregator to maximize its profit. The proposed mixed-integer linear hybrid stochastic-robust model improves the evaluation of DR aggregator's scheduling for the probable worst-case scenario. Finally, to demonstrate the effectiveness of the proposed approach, the model is thoroughly simulated in a real case study.

The increasing penetration of renewable energy sources in areas with wholesale energy markets may have significant impacts on the prices of electricity within these markets. These renewable energy sources typically have low or zero marginal prices and thus can bid into energy markets at prices which might be below plants using other generating technologies. This work seeks to understand the impact of these zero marginal cost plants in the Iberian Energy Market. This work makes use of an Artificial Neural Network (ANN) to evaluate the impact of growing renewable energy generation on the market-clearing price. Real data from the Iberian Energy Market is chosen and used to train the ANN. The scenarios used for renewable energy generation are taken from the newly published national energy and climate plans for both Spain and Portugal. Results show that increasing penetration of renewable energy leads to significant reductions in the forecasted energy price, showing a price decrease of about 23 € /MWh in 2030 compared to the baseline. Increasing solar PV generation has the largest effect on market prices.

This paper models a novel demand response (DR) trading strategy. In this model, the DR aggregator obtains the DR from the end-users via two types of DR programs, i.e. a time-of-use (TOU) program and an incentive-based DR program. Then, it offers this DR to the wholesale market. Three consumer sectors, namely residential, commercial and industrial, are included in this problem. The DR program is dependent on their corresponding load profiles during the studied time horizon. This paper uses a mixed-integer linear programming (MILP) problem and it is solved using the CPLEX solver through a stochastic programming approach in GAMS. The risk measure chosen to represent the load uncertainty of the users who are participating in the DR program is Conditional Value-at-Risk (CVaR). The proposed problem is simulated and assessed through a case study of a test system. The results indicate that the industrial loads play a major role in the power system and this directly affects the DR program. Moreover, the risk-averse decision-maker in this model favors a reduced participation in the DR programs when compared to a decision-maker who is risk-neutral, since the risk-averse decision maker prefers to be more secure against uncertainties. In other words, an increase in risk factor results in a decrease in the participation rate of the consumers in DR programs.

This paper focuses primarily on the flexibility of active prosumers in an islanded microgrid operation. The main objective is finding the best strategy to implement on an existing medium voltage grid, with several consumers, with the capability of producing some power for the grid operation, via Renewable Energy Resources (RES), or thermal Units, generally gas turbines, also there is the capability of some energy storage through batteries. Since power output of RES has a cost per kw of zero, it is greatly important to find the best combination of these resources who best suit the test system. For the purposes of these tests, the available investment funds are unlimited, although, there are some constraints regarding maximum RES penetration and ESS capacity.

In recent years, the energy sector has undergone major changes, particularly in Portugal, where there is complete liberalization of the electricity sector. Like in other European countries, a market agent has been created to facilitate trading relations between producer and trader. The Virtual Power Plant (VPP) agent aims to minimize the costs to the trader and maximizes the profits of producers. In this work, five renewable power plants, which are contractually linked with a VPP, are analyzed to verify the profitability of these contracts for both parties. Using this framework, an analysis is carried out examining the differences between actual renewable production and the planned (forecasted) production. In some instances, there are significant deviations between actual and forecast production and this results in higher costs. Consequently, the greater the deviations, the greater the expenses and, therefore, the lower the profit of each party. Thus, new bidding strategies that result in the reduction of these differences are sought. The bidding strategies proposed in this paper involve markets and various types of contracts to deliver the optimal solution that results in higher profits for both parties. The results show an increase in VPP profit on average of 32%

The problem of reconfiguration for active distribution systems is formulated as a stochastic mixed-integer second-order conic programming (MISOCP) model that simultaneously considers the minimization of energy power losses and CO2 emissions. The solution of the model determines the optimal radial topology, the operation of switchable capacitor banks, and the operation of dispatchable and non dispatchable distributed generators. A stochastic scenario-based model is considered to handle uncertainties in load behavior, solar irradiation, and energy prices. The optimal solution of this model can be reached with a commercial solver; however, this is not computationally efficient. To tackle this issue a novel methodology which explores the efficiency of classical optimization techniques and heuristic based on neighborhood structures, referred as matheuristic algorithm is proposed. In this algorithm. the neighborhood search is carried out using the solution of reduced MISOCP models that are obtained from the original formulation of the problem. Numerical experiments are performed using several systems to compare the performance of the proposed matheuristic against the direct solution by the commercial solver CPLEX. Results demonstrate the superiority of the proposed methodology solving the problem for large-scale systems.

Nowadays demand response (DR) is known as one of the main parts of the power system especially in the smart grid infrastructure. Furthermore, to enhance the participation of the consumers in the DR programs, the Independent System Operators (ISOs) have introduced a new entity, i.e. Demand Response Aggregator (DRA). The main contribution of this paper is to investigate a novel framework to increase the profits of the DRA participating in the day-ahead electricity market, i.e. employment of an axillary generation system in the DRA entity. It is supposed that the DRA in this paper has an axillary generation system and it would lead to an increase in the profit of the DRA through avoiding the economic loss in the process of trading DR obtained by the active participation of prosumers in the electricity market. The fuel cell is introduced as the axillary generation unit to the DRA unit. In the framework proposed in this paper, the DR is acquired from end-users during peak periods and will be offered to the day-ahead electricity market. The power flow during the off-peak hours is in another direction, i.e. from the grid to the consumers. In this model, the information-gap decision theory (IGDT) is chosen as the risk measure. The uncertain parameter is the day-ahead electricity market price. The optimization problem’s objective is to maximize the profit of the DRA. The behavior of the risk-seeker decision-maker is analyzed and investigated. The feasibility of the program is demonstrated by applying it to realistic data.

Self-scheduling of Home Energy Management Systems (HEMS) is one of the most interesting problems for active end-users to reduce their electricity bills. The electricity bill reduction by adopting Demand Response Programs (DRP) considering the flexibility of the end-users is addressed in this paper. The problem is addressed as a multi-objective optimization problem. The first objective function is the minimization of the daily bill, while the second objective aims to minimize the Discomfort Index (DI) regarding shifting the home appliances plugging-in time. The Time-of-Use (ToU) tariff is adopted in this paper and therefore, the end-users can benefit from shifting their flexible loads from peak hours to the off-peak hours and this reduces their bills, accordingly. In this case, the end-users have to change their energy consumption which imposes a level of discomfort on the end-users. Therefore, a two-stage model is proposed in this paper to deal with the mentioned objective functions. The proposed model is represented as standard mixed-integer linear programming (MILP) and for solving this problem the epsilon-constraint method is adopted in this study. The obtained Pareto front from the epsilon-constraint multi-objective framework is fed to the fuzzy satisfying method for final plan selection. These results show that by providing the Pareto set of optimal solutions to the user, they are more informed and can make decisions that better suit their preferences.

There is an ongoing paradigm shift occurring in the electricity sector. In particular, previously passive consumers are now becoming active prosumers and they can now offer important and cost-effective new forms of flexibility and demand response potential to the electricity sector and this can translate into system-wide operational and economic benefits. This work focuses on developing a model where prosumers participate in demand response programs through varying tariff schemes, and the model also quantifies the benefits of this flexibility and cost-reductions. This work includes transactive energy trading between various prosumers, the grid and the neighborhood. A stochastic tool is developed for this analysis, which also allows the quantification of the collective behavior so that the periods with the greatest demand response potential can be identified. Numerical results indicate that the optimization of energy transactions amongst the prosumers, and including the grid, leads to considerable cost reductions as well as introducing additional flexibility in the presence of demand response mechanisms.

The integration of new technologies at the residential level such as energy storage systems, electric vehicles, solar photovoltaic generation and mini wind turbines triggered the appearance of a new agent in the power systems called prosumers. This agent has the potential to provide new forms of flexibility and cost-effective solutions. However, associated with these new solutions there are also a number of problems that affect these solutions, particularly network constraints. This work presents an analysis not only on the benefits of utilizing the prosumer's flexibility but also to the problems associated with the operation and optimization of the network. A new model is presented that considers energy transactions between prosumers in the neighborhood and between them and the network using on a stochastic framework, in order to account for a set of uncertainties in the form of scenarios associated with the availability of various resources and technologies. The results show the economic benefit of energy transactions between prosumers resulting in more flexibility for the system while highlighting the effect of network restrictions and potential problems associated with them.

This chapter provides a panorama of potential applications of the blockchain technology to the energy sector. The chapter provides an introduction to the blockchain technology covering the main technological characteristics and providing some context to what is a rapidly emerging technology. Some limitations of the technology are also discussed along with key issues relating to the regulation of the technology. The chapter then highlights how the blockchain technology could impact the energy sector in five distinct subsectors, these being energy trading, environmental attribute management, electric mobility, and financing considerations. The chapter then concludes by providing an up-to-date list of all nearly 150 companies working on the blockchain in energy space.

This paper investigates a fully decentralized model for electricity trading within a transactive energy market. The proposed model presents a peer-to-peer (P2P) trading framework between the clients. The model is incorporated for industrial, commercial, and residential energy hubs to serve their associated demands in a least-cost paradigm. The alternating direction method of multipliers (ADMM) is implemented to address the decentralized power flow in this study. The optimal operation of the energy hubs is modeled as a standard mixed-integer linear programming (MILP) optimization problem. The corresponding decision variables of the energy hubs operation are transferred to the peer-to-peer (P2P) market, and ADMM is applied to ensure the minimum data exchange and address the data privacy issue. Two different scenarios have been studied in this paper to show the effectiveness of the electricity trading model between peers, called integrated and coordinated operation modes. In the integration mode, there is no P2P energy trading while in the coordinated framework, the P2P transactive energy market is taken into account. The proposed model is simulated on the modified IEEE 33-bus distribution network. The obtained results confirm that the coordinated model can efficiently handle the P2P transactive energy trading for different energy hubs, addressing the minimum data exchange issue, and achieving the least-cost operation of the energy hubs in the system. The obtained results show that the total operating cost of the hubs in the coordinated model is lower than that of the integrated model by $590.319, i.e. 11.75 % saving in the costs. In this regard, the contributions of the industrial, commercial, and residential hubs in the total cost using the integrated model are $3441.895, $596.600, and $988.789, respectively. On the other hand, these energy hubs contribute to the total operating cost in the coordinated model by $2932.645, $590.155, and $914.165 respectively. The highest decrease relates to the industrial hub by 14.8 % while the smallest decrease relates to the residential hub by 1 %. Furthermore, the load demand in the integrated and coordinated models is mitigated by 13 % and 17 %, respectively. These results indicate that the presented framework could effectively and significantly reduce the total load demand which in turn leads to reducing the total cost and power losses.

Due to technological advances, the growing need for a decarbonized economy, and the desire to reduce urban air pollution, electric vehicles (EVs) are seen as promising developments for the progressive decarbonization of the transport sector. The potential for large scale integration of solar photovoltaic (PV) systems has not been explored fully, compared to other renewable energy uses in power systems. One of the proposed paths for the sustainable integration of EVs and solar exploitation in power systems involves the creation or installation of parking lots fitted with solar PV systems on their rooftops. This concept increases the reliability and robustness of the power system, as studies show that EVs are parked for the vast majority of the time. The EVs can then be optimally managed to assist the network in critical moments. The solar parking lot would then serve as a backup to manage the EVs' state-of-charge (SoC) which would guarantee the owners’ comfort and help to accelerate the decarbonization of the economy. The present work presents a comprehensive survey of the state-of-the-art concepts of photovoltaic (PV) panels, EVs and batteries, and how the different associated technologies can be applied in the concept of solar parking lots.

Electric vehicles (EVs) are seen as a crucial tool to reduce the polluting emissions caused by the transport and power systems (PS) sector and the associated shift to a cleaner and more sustainable energy sector. The combination of EVs and solar photovoltaics (PV) in PS, specifically through the aggregation of EVs in parking lots (PLs), may improve the reliability and flexibility of the PS, assisting the power network in critical moments. This work proposes a novel aggregator agent in the energy system which is an EV charging station with an installed PV system. In this work, an optimal operation strategy for the solar-powered EV PL (EVSPL) operation is presented. The model optimizes the EVSPL's participation in various energy and ancillary services markets, including the effects of capacity payments. The results show that the EVSPL leads to higher profits. The EVSPL's participation in ancillary services is highly influenced by the prices. The results of this work show that this novel agent can actively participate in the energy system in an economically viable manner while respecting the technical constraints of the network and providing important ancillary services to the system operator.

This paper focuses on the long-term planning of power systems considering the impacts of Electrical Energy Storage Devices (ESSD) as well as Demand Response Programs (DRPs). The proposed model incorporates a two-stage optimization strategy in order to reduce the computational burden of the nonlinear problem. The upper-level of optimization model includes investment decision variables (long-term planning) while in the lower-level, the optimal operation of the model for short-term horizon has been addressed. In the operational stage, the optimal scheduling of power system in the presence of suggested ESSD size and location from the upper level is evaluated. Moreover, the Time-of-Use (ToU) Demand Response (DR) pricing scheme has been applied in the operational stage to evaluate its capability to reduce the total operating costs. The simulation results on the standard 6-bus test system validates the applicability of the proposed two-stage optimization model and illustrates that the optimal sizing and location of ESSDs along with DRP implementation could effectively reduce the total systems costs and improve the power system load factor.

This paper proposes a novel method for solving the Optimal Power Flow (OPF) problem in conditions close to realtime. The linearized cost function of the generating units is used to this end. Besides, the presented linear model is solved using the Consensus Alternating Direction Method of Multipliers (C-ADMM) approach. This technique would provide the possibility of modeling the problem both in centralized and decentralized manners. The suggested method exploits the power flow results obtained from the previous iteration to considerably improve the rate of convergence. As the C-ADMM method uses an iterative technique, Lagrange multipliers, and the norm function, the rate of convergence highly depends upon assigning the initial conditions and the optimality gap. Thus, using the operating points of the previous instant due to being close to the operating point of the current instant would enhance the results. The proposed model has been implemented on two case studies including the Pennsylvania-New Jersey-Maryland (PJM) network to verify the results and the 9-bus system to evaluate the performance of the model for the daily operation.

This paper presents a self-scheduling model for home energy management systems (HEMS) in which a novel formulation of a linear discomfort index (DI) is proposed, incorporating the preferences of end-users in the daily operation of home appliances. The HEMS self-scheduling problem is modelled as a mixed-integer linear programming (MILP) multi-objective problem, aimed at minimizing the energy bill and DI. In this framework, the proposed DI determines the optimal time slots for the operation of home appliances while minimizing end-users’ bills. The resulting multi-objective optimization problem has then been solved by using the epsilon-constraint technique and the VIKOR decision maker has been employed to select the most desired Pareto solution. The proposed model is tested considering tariffs in the presence of various price-based demand response programs (DRP), namely time-of-use (TOU) and real-time pricing (RTP). In addition, different scenarios considering the presence of electrical energy storage (EES) are investigated to study their impact on the optimal operation of HEMS. The simulation results show that the self-scheduling approach proposed in this paper yields significant reductions in the electricity bills for different electricity tariffs. Post-print / Final draft

This paper presents a self-scheduling framework, using a risk-constrained optimization model for the home energy management system (HEMS), considering fixed, controllable, and interruptible loads, as a new contribution to earlier studies. The objectives are reducing the electricity bill and managing the risk of purchasing energy over on-peak hours and prosumer's discomfort index (DI) due to shifting load to undesired hours. In this regard, the problem formulation is represented as a mixed-integer linear programming (MILP) model. Afterward, the proposed HEMS is promoted to a conditional value-at-risk (CVaR) model. The prosumer is equipped with an energy storage system and a solar photovoltaic (PV) panel. A substantial fraction of the load demand is controllable, and there is an inverter-based heating, ventilation, and air conditioning (HVAC), where HVAC is modeled as a variable-capacity interruptible load. The optimal scheduling of the loads is supposed to be done by the proposed HEMS, and the time-of-use (TOU) mechanism is utilized, including three price steps over the day. The results, obtained from thoroughly simulating the problem using household data, validate the performance of the presented HEMS in mitigating the amount of the electricity bill, while keeping the discomfort index of the prosumer at a desired level.

Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system’s operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.

A key challenge for future energy systems is how to minimize the effects of employing demand response (DR) programs on the consumer. There exists a diverse range of consumers with a variety of types of loads, such as must-run loads, and this can reduce the impact of consumer participation in DR programs. Multi-energy systems (MES) can solve this issue and have the capability to reduce any discomfort faced by all types of consumers who are willing to participate in the DRPs. In this paper, the most recent implementations of DR frameworks in the MESs are comprehensively reviewed. The DR modelling approach in such energy systems is investigated and the main contributions of each of these works are included. Notably, the amount of research in MES has rapidly increased in recent years. The majority of the reviewed works consider power, heat and gas systems within the MES. Over three-quarters of the papers investigated consider some form of energy storage system, which shows how important having efficient, cost-effective and reliable energy storage systems will be in the future. In addition, a vast majority of the works also considered some form of demand response programs in their model. This points to the need to make participating in the energy market easier for consumers, as well as the importance of good communication between generators, system operators, and consumers. Moreover, the emerging topics within the area of MES are investigated using a bibliometric analysis to provide insight to other researchers in this area.

There is a growing need for increased flexibility in modern power systems. Traditionally, this flexibility has been provided by supply-side technologies. There has been an increase in the research surrounding flexibility services provided by demand-side actors and technologies, especially flexibility services provided by prosumers (those customers who both produce and consume electricity). This work gathers 1183 peer-reviewed journal articles concerning the topic and uses them to identify the current state of the art. This body of literature was analysed with two leading textual and scientometric analysis tools, SAS© Visual Text Analytics and VOSviewer, in order to provide a detailed understanding of the current state-of-the-art research on prosumer flexibility. Trends, key ideas, opportunities and challenges were identified and discussed.

It can be difficult knowing where you stand when it comes to consumer law, especially from a business perspective. Matthew Gough provides an insight into the laws protecting you from unfair sales practices when purchasing for your care home

Ocean prediction systems rely on an array of assumptions to optimize their data assimilation schemes. Many of these remain untested, especially at smaller scales, because sufficiently dense observations are very rare. A set of 295 drifters deployed in July 2012 in the north-eastern Gulf of Mexico provides a unique opportunity to test these systems down to scales previously unobtainable. In this study, background error covariance assumptions in the 3DVar assimilation process are perturbed to understand the effect on the solution relative to the withheld dense drifter data. Results show that the amplitude of the background error covariance is an important factor as expected, and a proposed new formulation provides added skill. In addition, the background error covariance time correlation is important to allow satellite observations to affect the results over a period longer than one daily assimilation cycle. The results show the new background error covariance formulations provide more accurate placement of frontal positions, directions of currents and velocity magnitudes. These conclusions have implications for the implementation of 3DVar systems as well as the analysis interval of 4DVar systems. Published by Elsevier Ltd.

This study offers a new method for estimating High-Frequency (HF) radar surface current velocity error in data comparisons with other types of instrumentation. A new method is needed in order to remove the zero-mean random spatial and temporal fluctuations present in surface-current measurements from all sensors. Conventional methods for calculating radar error when comparing with another instrument have included their root mean square differences and scatter plots that provide correlation coefficient and slope/intercept of the regression line. It seems that a meaningful estimate of radar error should attempt to remove both sensors’ zero mean random fluctuations, inasmuch as possible. We offer and compare a method that does this. The method was tested on data collected in the Central San Francisco Bay, where GPS surface-drifter deployments were conducted within the coverage of four 42 MHz radars over six days in October of 2008. Drifters were continuously deployed in these areas over the sampling days, providing 525 usable drifter measurements. Drifter and radar measurements were averaged into thirty-minute time bins. The three-day long-term averages from the sampling areas were then subtracted from the thirtyminute averages to remove biases associated with comparisons done with short, disjoint time-sample periods. These were then used to develop methods that give radar error or bias after the random fluctuations have been removed. Results for error estimates in this study are commensurate with others where random fluctuations have been filtered, suggesting they are valid. The estimated error for the radars in the SF Bay is low, ranging from −7.57 cm/s to 0.59 cm/s.

PurposeThe Mental Capacity Act (MCA) was implemented in 2007 as a piece of legislation to empower and protect adults who require support making decisions. Many older adults in residential care homes will be in this position due to developmental disabilities associated with functional impairments of the mind and brain. This paper aims to evaluate the impact of MCA training within older persons' care homes within an East‐Midlands local authority.Design/methodology/approachSemi structured interviews were conducted with key informants who had strategic responsibility for implementation of MCA training as well as a focus group conducted with managers/deputy managers of care homes within the local authority.FindingsWith a primary focus on training, data revealed issues surrounding the delivery and content of training, and the organisational factors relating to both training and the subsequent implementation of the knowledge learned.Research limitations/implicationsThe key informants for this paper are limited to management perspectives. Interviews and a focus group were conducted with stakeholders who either had direct responsibility for service delivery or managerial oversight for training and development.Practical implicationsThe paper suggests methods of delivery with the Mental Capacity Act which offer a tailored, engaging and cost effective alternative to conventional “away day” training sessions.Originality/valueThe paper challenges and critiques conventional approaches to training the social care workforce.

Purpose – There are several tensions in the disclosure of personal experience of mental health problems. The issue of “otherness” is particularly pertinent when exploring mental health. This paper seeks to draw upon workshop discussion around the theme of educators disclosing personal experience of mental health problems.Design/methodology/approach – This paper is based around a critically reflective self‐selecting conference workshop for mental health educators, practitioners and others.Findings – There are risks and benefits to personal disclosure of lived experience of mental health problems. It was found that three areas emerged when considering the dilemmas of self‐disclosure: personal impact, wider ethical issues and student learning.Practical implications – The paper provides information on how educators can maximise the benefits and minimise the hazards associated within these areas as a means to develop good practice guidance and when and how to disclose personal experience.Originality/value – Th...

This paper presents a new form of dance notation that has a hand written and machine readable format: eXtensible Dance Scripting Notation (xdsn). The notation system is designed to be quick to write, simple to learn and able to handle complex, emergent and deconstructivist dance works. The machine readable format of xdsn is platform independent, searchable, and well formed to encourage the development of dance notation software. An overview of the system is given illustrating its context in current contemporary dance ontology and as a mechanism for notation driven avatar animation. eXtensible Dance Scripting Notation was developed by the author of this paper in direct response to a proposal from Michael Klein for alternative methods of dance scripting. The system (version 0.3), is currently in localised use and will be developed at the School of Computing Sciences, University of East Anglia, Norwich. This work will be undertaken as a Phd Research project under the leadership of Professor John Glauert.