The final chapter of Part III, which is the eighth chapter in the dissertation, follows the same structure as that of §7, but the numerical results obtained by employing the maximisation of the expectedenergy production scheduling criterion are presented instead. The first section of the chapter contains a presentation of the results returned by the exact solution approach after employing the piecewise linearisation approach described in §5. This presentation includes results for both the 21-unit test system and the IEEE-RTS. An optimal maintenance sched- ule for the piecewise linear approximation is provided in respect of the newly proposed GMS objective function as is an analysis of the manpower required and available system capacity associated with these solutions. The optimal solutions obtained are also compared with solu- tions from the literature for a GMS model involving the well-known minimisation of the sum of squared reserve margins as scheduling criterion. Sensitivity analyses are also performed in order to analyse the feasibility of the exact solution approach for small systems, such as the 21-unit test system and the IEEE-RTS, as was performed for the minimisation of the probability of unit failure scheduling criterion. In the second section of this chapter, the approximate solution approach results obtained when employing the maximisation of the expectedenergy production scheduling criterion are presented for both the 21-unit test system and the IEEE-RTS. In this section, the results of the experimental design followed to determine the best combination of parameters for use in the method of SA are presented for both benchmark systems are pre- sented. The same parameter ranges are considered for the maximisation of the expectedenergy production scheduling criterion as was the case for the minimisation of the probability of unit failure scheduling criterion in order to obtain the best combination. Thereafter, solutions are presented for the 21-unit
This paper provided a review of outage scheduling, examined the economic dispatch principle based on incremental cost curves and described the development of a practical MATLAB software tool. This developed software successfully performed maintenancescheduling, for a maximum of thirty-two generating units, seeking to either maximize or minimize the spinning reserve capacity whist satisfying the load demand as well as the maintenance window constraints for each generating unit. A very simple, user friendly GUI was developed. It highlighted the periods during the specified horizon when every generating unit under analysis was online and offline. Accompanying each produced schedule was an analysis of the total power available and the corresponding spinning reserve for every week. Another function successfully implemented was the economic dispatch (again for a maximum of 32 generating units) over an hourly period and a 12-hour period. The latter period provided the option for every generating unit available to be shutdown and or started up once during this period. Here, an hourly report is generated which suggests how the available generating units should be loaded to achieve minimal cost and maximum efficiency (minimum heat rate). The report also highlights these minimal cost and heat rate figures. The 5- year maintenance forecast for each generating unit and the process of querying the outage records (excel spreadsheets) to determine a generating unit’s status, were both successfully implemented.
which are used as reliability objective criteria for the formulation of GMS problem. In , maintenance of generator is scheduled so as to minimize the risk through the minimization of yearly value of LOLE is proposed and solved using GA. LOLP is taken as objective in formulating GMS problem and has been solved using method of cumulants . The deterministic reliability criterion of minimizing the sum of the squares of the reserve is considered in  and the meta-heuristic based hybrid approach is used to solve the GMS problem in which heuristic approach is combined with GA / SA hybrid to seed the initial population. The objectives of minimizing the total operating cost and leveling the reserve are considered and solved using new TS algorithm in . In , Leou proposed a new formulation in which the cost and reliability are considered as an index and GA is combined with SA and is implemented for solving the problem. Particle swarm optimization (PSO) is used for finding the good schedule for maintenance of generating units by considering leveling the reserve generation as objective [14, 15]. Ant colony optimization (ACO) inspired by the foraging behavior of ant colonies is implemented for solving GMS in . Knowledge based expert system is applied in  to schedule the generator for maintenance in which the knowledge has been built in consultation with experienced operators and are expressed by rules and logic representations. To include the uncertainties present in the GMS, the objectives and constraints are expressed in fuzzy notation and embedded with dynamic programming to find the units maintenance schedule . In , the objectives and constraints are fuzzified through the guidance of GA and the maintenance schedule for generating units is obtained with the help of fuzzy dynamic programming. Four objective criteria such as loss of load expectation, expected un-served energy, expected fuel cost and constraint
Power system components are made to remain in operating conditions by regular preventive maintenance. The task of generatormaintenance is often performed manually by human experts who generate the schedule based on their experience and knowledge of the system, and in such cases there is no guarantee that the optimal or near optimal schedule is found. The purpose of maintenancescheduling is to find the sequence of scheduled outages of generating units over a given period of time such that the level of energy reserve is maintained. This type of schedule is important mainly because other planning activities are directly affected by such decisions. In modern power systems, the demand for electricity has greatly increased with related expansions in system size, which has resulted in higher number of generators and lower reserve margins making the generatormaintenancescheduling (GMS) problem more complicated. The eventual aim of the GMS is the effective allocation of generating units for maintenance while maintaining high system reliability, reducing production cost, prolonging generator life time subject to some unit and system constraints -.
It was also noted that when a power system (such as Eskom) is under severe pressure due to low gross reserves (low installed capacity compared to expecteddemand), the reliability of the system is usually more important (so to avoid load shedding) than other objectives, such as minimising cost. As the gross reserves, however, become larger (a situation that Eskom is seeking to achieve with its planned increase of installed capacity), the reliability of the system becomes less pressing and other objectives (such as minimising cost) may become more important. MOO is desirable in this sense as it seeks to provide a best possible set of trade-off solutions. It was furthermore stated that it would be beneficial to include another GMS objective that measures the risk of unit failure (the LOLP, for example) within the MO paradigm proposed in this dissertation. The stakeholders mentioned that they would prefer the algorithm not to run too long — preferably overnight (12–16 hours) or perhaps during the day (± 8 hours). Another important point raised was that, as the gross reserves become larger, the cost of “dumping 3 ” electricity will become more prominent and should also be included in the production planning module. This may be achieved by including another virtual station (opposite in nature to the unmet virtual station) which has a certain cost rate (measured $/MWh) associated with dumping electricity and whose energy cost is included in the cost objective to be minimised.
One of the options available to the utilities in order to maintain a high level of reliability and economy of the power system is economic dispatch (ED). ED allocates the total power demand among the online generating units in order to minimize the cost of generation while satisfying important system constraints. Some factors that influence ED of the system are operating efficiency of generating units, fuel and operating costs, and transmission losses. The ED problems are in general non-convex optimization problems with many local minima. Numerous classical techniques such as LaGrange based methods, linear programming (LP), non- linear programming (NLP) and quadratic programming (QP) methods have been reported in the literature.
Since the events of 2008, South Africa has not experienced any load shedding, mainly due to the recovery plan implemented by Eskom. It comprised three phases — the first two phases being short-term solutions which ended in 2008. The current phase is a medium-term solution scheduled to last to 2012 when the first of the new base-load stations is expected to come online. Ultimately, the challenge remains to achieve and maintain a reserve margin of 15% . In view of the South African electricity challenge described above, a key area of concern is the planned maintenance outages of generation plants. Since planned maintenance is a power system requirement, it is an unavoidable duty for an electricity utility to perform. The relatively old age and higher load factor of the South African power stations, significantly increase the need for plant maintenance, thereby reducing the opportunity (leverage) for planned maintenance. Combined with the diminished safety margin of the capacity, these two factors render the task of scheduling planned maintenance outages of power generating units a daunting endeavour at best. Furthermore, scheduling the planned maintenance outages in such a way that the system supply still satisfies the demand, is the simplest form of the problem — additional factors and constraints may also influence the scheduling process, such as limited maintenance resources. The problem of finding a schedule for the planned maintenance outages of generating units in a power system is known as the generatormaintenancescheduling (GMS) problem. The challenges currently faced by Eskom in South Africa may easily occur in other power systems across the world. As power systems become larger and demand for electricity increases continually, so does the difficulty in finding maintenance schedules increase in complexity, especially in systems with small reserve margins and/or high levels of constriction.
The course covers the advanced methods and applications that a suitably qualified professional would use in carrying out fully functional plant maintenance. In summary, the course provides a step-by-step practical guide to best practices of maintenance planning and scheduling that will essentially reduce maintenance costs and deliver maximum business benefits.
Maintenance staffing objectives include minimum cost, maximum availability, maximum reliability, or a combination of these measures. Dietz and Rosenshine (1997) developed a method to determine the optimal structure of a maintenance workforce, and used it to maximize military aircraft sortie generation subject to a limit on maintenance staffing cost. Hecht et al. (1998) presented a queueing model to determine average outage time in US air traffic control system as a function of the number of maintenance technicians assigned to each center. Galpin et al. (1993) surveyed operation and maintenance staffing practices in utility plants and compared theoretical and actual staffing levels. Al-Zubaidi and Christer (1997) constructed a maintenance manpower simulation model to estimate the costs of different manpower management and operational procedures. Duffuaa and Al-Sultan (1997) proposed mathematical programming approaches for planning and schedulingmaintenance resources, including manpower, equipment, and parts.
The emergence of smart homes enables energy providers to develop sophisticated energy management solutions, in attempt to optimise energy production while providing home users with increased comfort and potential cost reduction. The future smart homes will be equipped with a range of control devices and sensing/actuating systems capable of working together in automatic way to perform some pre-defined functions. Over the past decade, the majority of technical challenges for the home hardware and software solutions have been solved, and a range of commercial products is available. For energy providers, the greatest remaining challenges lie in: (1) development of intelligent resource management algorithms to optimise the energy consumption, both at the single- household level and at the large-scale level; (2) establishing increased level of trust with the user by ensuring that the users’ energy consumption data is kept secret. This paper addresses both of these issues
Greenhouse gas emissions are posing a serious concern around the world due to their impact on the environment and climate change. The global economy, on the other hand, is in the midst of unprecedented demand for energy requiring new investments for the reinforcement and expansion of grid infrastructures and the large adoption of renewable energy resources. Consequently, energy prices are set to gradually increase. Demand Response (DR) is considered as an important element that can help customers control their energy usage . Engaging consumers in DR will reduce power consumption during peak demand hours and provides savings in electricity bills . DR can be defined as a change in electricity consumption pattern of end-users by increasing or decreasing the loads in response to tariff signals and other incentives from the energy supplier , . However, residential consumers do not want to spend time on calculating and analysing their power consumption and scheduling their household devices to save money . Therefore, the grid communication infrastructure and smart appliances must to be able to respond to any electricity consumption scenario envisaged by consumers. Home Energy Management System (HEMS) is a demand response tool that improves the energy utilisation and seeks to reduce electricity cost by shifting and curtailing demand in response to electricity tariffs and consumer comfort during peak hours , , , . Many researchers are working towards developing efficient and
algorithm, with bounded number of processors. The LDCP is a list basedscheduling algorithm it works on key tasks are identified and scheduled based on the minimum schedule length generated. The algorithm has three phases the task selection phase, processor selection phase and the status update phase. Time complexity of the LDCP algorithm is of O (m*n3) where m is number of processors and n is the number of tasks. The comparative study showed that the LDCP algorithm is better than the HEFT and LDS algorithm. Hamid et al.  proposed a novel list basedscheduling algorithm for heterogeneous computing environment. The algorithm is called predict earliest finish time (PEFT). The algorithm has a complexity comparable to most of the existing state of art algorithms but offers a better makespan. This has been achieved by using a feature called lookahead which helps to calculate the optimistic cost table (OCT) without increasing the complexity. The algorithm is based on the OCT and is used for both task selection and processor assignment. There are three phases to it namely optimistic cost table calculation, task prioritization phase and processor selection phase. Remainder of the paper is organized as follows: Section 3 illustrates the Models to be followed. Section 4 describes the proposed algorithm. Section 5 represents the simulation results and analysis Section 6 shows the conclusion and future scope.
calculated in terms of schedule displacement from an exhaustive sensitivity analysis with respect to the model below, without the two sets of fairness constraints. The values can be thought of as a request’s contribution to the violation of capacity constraints. We then require the proportion of schedule displacement assigned to an airline to be proportional to a weighted sum of the marginal costs of its requests via MDA constraints (10). By only counting requests which increase total displacement, airlines are not penalised for making requests in off-peak periods, where there is sufficient capacity to meet demand.
The establishment of the relation matrix E n e re- pair between maintenance type and workshop 0- 1Since motor cars with different maintenance levels enter different workshops in a different or- der, in which e = 1, 2, 3, 4, 5 denotes the
integer nonlinear model to find the best working schedule based on product quality cost and workers reliability. They assumed that the workers have a specific fatigue limit and the workers can rest to elevate their fatigue. Qiong et al.  presented a meta- heuristic model to tackle the limitation on human resources in parallel machine scheduling problem with precedence constraints. Bouzidi-Hassini et al.  discussed a new approach to schedule the production and simultaneously maintenance operations. This approach takes into account human resources parameters such as availability and skills. Zhu et al.  studied a single machine scheduling problem that the process time of each job is dependent on resource allocation function, job position in the sequence and rate-modifying activity. Although, there are other research papers that investigate the human resource in scheduling, most of them considered human as a common resource like other resources such as equipment and raw material.
DOI: 10.4236/sgre.2018.91002 17 Smart Grid and Renewable Energy Challenges facing traditional grids have paved the way for Smart Grid vision. Smart grids are characterized by bidirectional flow of information and electrici- ty. Smart grid promises benefits such as improved grid efficiency, resilience, re- liability, self-healing ability, demand management, increased consumer choice and possibility of new products, services and markets. For benefits associated with smart grid to be realized, a number of challenges have to be addressed. For instance, smart grid necessitates overhaul of all existing electric meters and re- placing them with smart meters which are relatively more expensive. Even ap- pliances will have to be replaced with smart ones-capable of responding to con- sumer settings and control signals from utilities. It is likely that consumers will be resistant to these changes because of fear of the unknown and privacy. How- ever, factors such as decreasing cost of smart meters and smart appliances, im- proving access to communication infrastructures and increasing awareness about electricity management means there is a good chance of changes to be embraced by both consumers and utilities .
The expected change in houses after complete implementation of smart grid and smart meters at the consuming side includes communication and embedded control. Using these two avail- able technologies, applying the approach of generalized tit for tat, game theory concept will ensure that the above stated problem is reduced considerably. It can also be applied to very large scale consumers since the technique is versatile. Consider a situation that involves a utility company and several residen- tial consumers. The utility company first estimates the electric- ity demand for the next hours and sets the price for different hours. Then, the utility company advertises these prices to the customers over the supporting digital communication net- work. Then, each user optimizes its energy consumption by adopting the best scheduling for its appliances based on this received price information from the utility company.
ABSTRACT:Automated demand side management encourage the consumer to use optimal energy during peak hours, or to move the time of energy use to off-peak times such as night time and weekends. Peak demand management does not necessarily decrease total energy consumption, but could be expected to reduce the need for investments in networks and/or power plants for meeting peak demands. Energy storage units helps to store energy during off-peak hours and discharge them during peak hours. For successful DSM, the study and analysis of consumer’s energy usage habits is necessary. This paper offers the ideas to shift the load of consumer from peaks to valleys i.e. to even out the load curve . It also focuses on the important aspects of DSM such as, load prediction, load scheduling, and pilferage detection and communication aspects to improve the revenue collection fromconsumers. Automated DSM is based on short term load prediction and load scheduling with the help of smart meters and smart sensors for the exchange of data between consumer and utility to enhance the perpetuity of the concept of smart grid.
Smart metering infrastructure allows for two-way communication and power transfer. Based on this promising technology, we propose a demand-side management (DSM) scheme for a residential neighbourhood of prosumers. Its core is a discrete time dynamic game to schedule individually owned home energy storage. The system model includes an advanced battery model, local generation of renewable energy, and forecasting errors for demand and genera- tion. We derive a closed-form solution for the best response problem of a player and construct an iterative algorithm to solve the game. Empirical analysis shows exponential convergence towards the Nash equilibrium. A comparison of a DSM scheme with a static game reveals the advantages of the dynamic game approach. We provide an extensive analysis on the influence of the forecasting error on the outcome of the game. A key result demonstrates that our approach is robust even in the worst-case scenario. This grants considerable gains for the utility company organising the DSM scheme and its participants.