Energy-saving policies for temperature-controlled production systems with state-dependent setup times and costs

There are numerous practical examples of production systems with servers that require heating in order to process jobs. Such production systems may realize considerable energy savings by temporarily switching off the heater and building up a queue of jobs to be processed later, at the expense of extra queueing costs. In this paper, we optimize this trade-off between energy and queueing costs. We model the production system as an M/G/1 queue with a temperature-controlled server that can only process jobs if a minimum production temperature is satisfied. The time and energy required to heat a server depend on its current temperature, hence the setup times and setup costs for starting production are state dependent. We derive the optimal policy structure for a fluid queue approximation, called a wait-heat-clear policy. Building upon these insights, for the M/G/1 queue we derive exact and approximate costs for various intuitive types of wait-heat-clear policies. Numerical results indicate that the optimal wait-heat-clear policy yields average cost savings of over 40% compared to always keeping the server at the minimum production temperature. Furthermore, an encouraging result for practice is that simple heuristics, depending on the queue length only, have near-optimal performance

A Green Hydrogen Energy System:Optimal control strategies for integrated hydrogen storage and power generation with wind energy

The intermittent nature of renewable energy resources such as wind and solar causes the energy supply to be less predictable leading to possible mismatches in the power network. To this end, hydrogen production and storage can provide a solution by increasing flexibility within the system. Stored hydrogen as compressed gas can either be converted back to electricity or it can be used as feed-stock for industry, heating for built environment, and as fuel for vehicles. This research is the first to examine optimal strategies for operating integrated energy systems consisting of renewable energy production and hydrogen storage with direct gas-based use-cases for hydrogen. Using Markov decision process theory, we construct optimal policies for day-to-day decisions on how much energy to store as hydrogen, or buy from or sell to the electricity market, and on how much hydrogen to sell for use as gas. We pay special emphasis to practical settings, such as contractually binding power purchase agreements, varying electricity prices, different distribution channels, green hydrogen offtake agreements, and hydrogen market price uncertainties. Extensive experiments and analysis are performed in the context of Northern Netherlands where Europe’s first Hydrogen Valley is being formed. Results show that gains in operational revenues of up to 51% are possible by introducing hydrogen storage units and competitive hydrogen market-prices. This amounts to a €126,000 increase in revenues per turbine per year for a 4.5 MW wind turbine. Moreover, our results indicate that hydrogen offtake agreements will be crucial in keeping the energy transition on track

Coordinating technician allocation and maintenance routing for offshore wind farms

A maintenance activity at offshore wind farms requires a combination of technicians with different skills. At an operational level, it is important to fully utilize and coordinate technicians in order to increase efficiency of the short-term maintenance planning. In this paper, we investigate sharing of technicians between wind farms over multiple periods, while determining per period vessel routes for delivering and picking up technicians. The problem can be considered as a novel variant of the multi-period multi-commodity pick up and delivery problem. We develop an adaptive large neighborhood search heuristic which achieves high-quality, and often optimal, solutions on benchmark instances from the literature. The heuristic is used to explore the benefits of different sharing policies. By sharing technicians, both the flexibility of the daily planning is improved and the expected maintenance costs are reduced. In addition, the increased flexibility results in fewer vessel trips and increases the decision maker’s ability to cope with extreme scenarios encountered in the short-term maintenance planning

Evaluating resource sharing for offshore wind farm maintenance:The case of jack-up vessels

Offshore wind energy is recognised globally as a viable alternative to finite energy sources. However, large cost reductions are still needed, particularly in the Operations & Maintenance (O&M) phase, which currently accounts for about 30% of the cost of offshore wind. For large component replacements, a jack-up vessel is often leased from the spot market, resulting in high costs and low utilisation. These costs can be lowered when multiple wind farm service providers would share the resources needed to employ jack-up vessels. In this paper, we analyse two types of resource sharing, as an alternative to each service provider leasing its own vessel: (i) vessel purchasing and sharing and (ii) the combined use of vessel and harbour sharing. We design a simulation model and include stochastic processes such as weather patterns and component failures. Results show that cost benefits up to 45% can be achieved compared to a leasing policy, depending on the number of wind farm service providers involved and on the geographical distance between offshore wind farms. Moreover, it is shown that the jack-up vessel should not be fully utilised to minimise costs. The performance benefits of harbour sharing in addition to vessel sharing are generally small, but become more significant if the network faces considerable congestion. Results are illustrated using a case study based on a setting in the Western North Sea

Condition-Based Production Planning:Adjusting Production Rates to Balance Output and Failure Risk

Problem Definition: Many production systems deteriorate over time as a result of load and stress caused by production. The deterioration rate of these systems typically depends on the production rate, implying that the equipment's deterioration rate can be controlled by adjusting the production rate. We introduce the use of condition monitoring to dynamically adjust the production rate to minimize maintenance costs and maximize production revenues. We study a single-unit system for which the next maintenance action is scheduled upfront. Academic/Practical Relevance: Condition-based maintenance decisions are frequently seen in the literature. However, in many real-life systems, maintenance planning has limited flexibility and cannot be done last minute. As an alternative, we are the first to propose using condition information to optimize the production rate, which is a more flexible short-term decision. Methodology: We derive structural optimality results from the analysis of deterministic deterioration processes. A Markov decision process formulation of the problem is used to obtain numerical results for stochastic deterioration processes. Results: The structure of the optimal policy strongly depends on the (convex or concave) relation between the production rate and the corresponding deterioration rate. Condition-based production rate decisions result in significant cost savings (by up to 50%), achieved by better balancing the failure risk and production output. For several systems a win-win scenario is observed, with both reduced failure risk and increased expected total production. Furthermore, condition-based production rates increase robustness and lead to more stable profits and production output. Managerial Implications: Using condition information to dynamically adjust production rates provides opportunities to improve the operational performance of systems with production-dependent deterioration

Seasonal hydrogen storage decisions under constrained electricity distribution capacity

The transition to renewable energy systems causes increased decentralization of the energy supply. Solar parks are built to increase renewable energy penetration and to supply local communities that become increasingly self-sufficient. These parks are generally installed in rural areas where electricity grid distribution capacity is limited. This causes the produced energy to create grid congestion. Temporary storage can be a solution. In addition to batteries, which are most suitable for intraday storage, hydrogen provides a long-term storage option and can be used to overcome seasonal mismatches in supply and demand. In this paper, we examine the operational decisions related to storing energy using hydrogen, and buying from or selling to the grid considering grid capacity limitations. We model the problem as a Markov decision process taking into account seasonal production and demand patterns, uncertain solar energy generation, and local electricity prices. We show that ignoring seasonal demand and production patterns is suboptimal. In addition, we show that the introduction of a hydrogen storage facility for a solar farm in rural areas may lead to positive profits, whereas this is loss-making without storage facilities. In a sensitivity analysis, we show that only if distribution capacity is too small, hydrogen storage does not lead to profits and reduced congestion at the cable connection. When the distribution capacity is constrained, a higher storage capacity leads to more buying-related actions from the electricity grid to prevent future shortages and to exploit price differences. This leads to more congestion at the connected cable and is an important insight for policy-makers and net-operators

A solution approach for deriving alternative fuel station infrastructure requirements

When an alternative fuel is introduced, the infrastructure through which that fuel is made available to the market is often underdeveloped. Transportation service providers relying on such infrastructures are unlikely to adopt alternative fuel vehicles as it may impose long detours for refueling. In this paper, we design and apply a new solution approach to derive minimum infrastructure requirements, in terms of the number of alternative fuel stations. The effectiveness of our approach is demonstrated by applying it to the case of introducing liquefied natural gas (LNG) as a transportation fuel in The Netherlands. From this case, we learn that, depending on the driving range of the LNG trucks and the size of area on which those trucks operate, a minimum of 5-12 LNG fuel stations is necessary to render LNG trucks economically and environmentally beneficial

Coordinating technician allocation and maintenance routing for offshore wind farms

A maintenance activity at offshore wind farms requires a combination of technicians with different skills. At an operational level, it is important to fully utilize and coordinate technicians in order to increase efficiency of the short-term maintenance planning. In this paper, we investigate sharing of technicians between wind farms over multiple periods, while determining per period vessel routes for delivering and picking up technicians. The problem can be considered as a novel variant of the multi-period multi-commodity pick up and delivery problem. We develop an adaptive large neighborhood search heuristic which achieves high-quality, and often optimal, solutions on benchmark instances from the literature. The heuristic is used to explore the benefits of different sharing policies. By sharing technicians, both the flexibility of the daily planning is improved and the expected maintenance costs are reduced. In addition, the increased flexibility results in fewer vessel trips and increases the decision maker’s ability to cope with extreme scenarios encountered in the short-term maintenance planning