Demand Side Management Initiatives Research Articles

Energy Efficiency: The Market for Energy Management Systems in Nigeria

Energy efficiency and demand side management (EEDSM) are critical components of sustainable energy strategies globally. In Nigeria, implementing these practices is crucial to address the growing energy demand, enhance energy security, and mitigate environmental impacts. This paper explores Nigeria's Energy Management Systems (EMS) market, highlighting the current state of energy efficiency and demand side management initiatives, market drivers, barriers, and future prospects. The study emphasises the importance of EMS in optimising energy use and underscores the urgent need for policy recommendations. The analysis shows that despite significant challenges, there are substantial opportunities for growth in the EMS market, driven by the industrial, commercial, and public sectors. However, these opportunities can only be fully realised with the right policy support, underlining the necessity of immediate action in this area and making the audience feel the situation's urgency.

Energy performance enhancement in institutional and commercial buildings

The built environment has been identified as a major contributor to the energy consumption as well as the GHG emissions across the world. Enhancing the energy performance of the building sector is crucial in meeting the current climate action targets, as well as in improving the economic outcomes and welfare of building owners and occupants. With the current energy and foreign exchange reserve crises faced by Sri Lanka, it is clearly evident that reducing the energy use in buildings as well as the reliance on imported fossil fuels are important. When it comes to greenhouse gas emissions curbing and climate action initiatives, demand side management initiatives appear to be more promising for Sri Lanka compared to supply side interventions, as the latter have higher costs of mitigation [3]. The effectiveness of the energy efficiency enhancement strategies employed in buildings depends on the archetype of the building as well as locational parameters such as climate conditions [1].

A Demand Side Management Control Strategy Using RUNge Kutta Optimizer (RUN)

Demand side management initiatives have gained attention recently because of the development of the smart grid and consumer-focused regulations. The demand side management programme has numerous goals. One of the main goals is to control energy demand by altering customer demand. This can be done in several ways, including financial discounts and behaviour changes brought about by providing knowledge to support the grid’s stressed conditions. In this study, demand side management techniques for future smart grids are presented, including load shifting and strategic conservation. There are many controlled devices on the grid. The load shifting and day before strategic conservation approaches mentioned in this study are derived analytically for the minimization problem. For resolving this minimization issue, the RUNge Kutta optimizer (RUN) was developed. On a test smart grid with two service zones, one with residential consumers and the other with commercial consumers, simulations are performed. By contrasting the outcomes with the slime mould algorithm (SMA), Sine Cosine Algorithm (SCA), moth–flame optimization (MFO), and whale optimization algorithm (WOA), RUN demonstrates its effectiveness. The simulation findings demonstrate that the suggested demand side management solutions produce significant cost savings while lowering the smart grid’s peak load demand.

Assessment of multiple-based demand response actions for peak residential electricity reduction in Ghana

Demand-side management initiatives such as voluntary demand response provide significant energy savings in the residential sector, which is a major peak demand contributor. The potential of such savings remains unexplored in Ghanaian households due to insufficient electricity consumption data, lack of end-user behavior information and knowledge about the cost-effectiveness of such programs. This research combines 80 household survey information and energy use monitoring data of household appliances, to assess the residential demand response potential of Ghana. A bottom-up approach based on modified end-use model is used to develop aggregate hourly load curve. The estimated electricity consumption is categorized based on their degree of control to determine peak demand reduction potential for the period 2018-2050. The average daily peak load reduction ranged between 65-406 MW representing 2-14% for the considered scenarios by 2050. The results show appreciable economic viability for investment in demand response with net present value varying between 28-645 million US$. We find that price, energy security and environment signals influence end-users’ electricity use behavior. Authors observe that for energy and cost savings to be realized, utility providers and consumers need effective cooperation on information delivery and feedbacks, and consumers should be incentivized to balance the benefits.

Economic assessment of smart grid initiatives for island power systems

Islands are facing considerable challenges in meeting their energy needs in a sustainable, affordable and reliable way. The present paper develops an integrated approach to economically assess initiatives that can transform island power systems into smart ones. Single and multi-action initiatives fostering the deployment of renewable energy sources (RES), energy storage systems (ESS), demand-side management (DSM), and electric vehicle (EV) are considered. An hourly unit commitment on a weekly basis is proposed to assess the impact of the initiatives on the system operation costs of five prototype island power systems, which have been identified by applying clustering techniques to a set of sixty islands power systems. The different investment costs of the initiatives are accounted for determining their corresponding internal rate of return (IRR) through their lifetime. The economic assessment of single and multi-action initiatives for five prototype islands representing sixty island power systems quantifies which initiatives are most suitable for which type of island power system. The assessment shows that islands of different sizes and features require different initiatives. Larger islands tend to DSM initiatives, whereas smaller islands tend to RES initiatives. Multi-action initiatives achieve highest system operation cost reduction, whereas single action initiatives yield to highest IRR.

Promoting energy efficiency in a South African university

Electricity supply issues have resulted in widespread blackouts and increased utility costs in South Africa. This is placing financial pressure on universities as they have limited means of increasing their income to cover the additional energy costs and, at the same time, are energy-intensive due to peculiar usage patterns and sprawling campuses with many (and often large) buildings. Thus, they must become energy-efficient. This is a case study of one such attempt. Four main findings emerged. Firstly, energy demand side management (DSM) had to be implemented in distinct phases due to unforeseen implementation hurdles. Secondly, there are both barriers and enablers to becoming an energy-efficient campus; that is, DSM requires managerial buy-in, capacitated operational personnel and money. Thirdly, personnel can either support or hinder DSM implementation. So, while hiring dedicated, skilled personnel to harness organisational commitment to DSM is essential, all personnel need training in energy-efficient behaviour and should be held accountable for DSM initiatives within their sphere of influence. An energy champion – at the highest level of the organisation – to influence policy and drive the behavioural and structural changes required, is strongly recommended. Lastly, DSM technologies may be readily available but are not necessarily bought, installed or used correctly due to behavioural and institutional cultural constraints.

Modeling of residential lighting load profile using adaptive neuro fuzzy inference system (ANFIS)

ABSTRACTMost practices currently adopted in modeling the presence of people in residential houses do not reflect the complexity of the impact occupants have on lighting loads. Hence the needs to contribute a methodology, that will be able to look into such characteristics. This study entailed the use of adaptive Neuro Fuzzy inference system (ANFIS) for estimation and prediction of lighting load usage profile for energy and demand side management initiatives. Results obtained showed a better correlation analysis and root mean square error (RMSE) in contrast to the regression method model. This shows that ANFIS model has good prediction accuracy capability.

A Survey of Communications and Networking Technologies for Energy Management in Buildings and Home Automation

With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes.

An optimal control model for load shifting – With application in the energy management of a colliery

This paper presents an optimal control model for the load shifting problem in energy management and its application in a South African colliery. It is illustrated in the colliery scenario that how the optimal control model can be applied to optimize load shifting and improve energy efficiency through the control of conveyor belts. The time-of-use electricity tariff is used as an input to the objective function in order to obtain a solution that minimizes electricity costs and thus maximizes load shifting. The case study yields promising results that show the potential of applying this optimal control model to other industrial Demand Side Management initiatives.

Interruptible load analysis using sequential Monte Carlo simulation

Electric utilities are increasingly examining cost-effective opportunities to utilise demand-side management initiatives, such as interruptible contracts to reduce the need for additional capacity. Techniques are presented for conducting interruptible load analysis using sequential Monte Carlo Simulation. A bisection search approach was developed to effectively determine the interruptible load carrying capability. An index, designated the avoidable additional generating capacity, is proposed and a procedure to calculate it is presented. Numerical studies were performed to indicate the possible applications of the proposed techniques.